diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..d6d7754bd6a8b60e105b918c44467ab63f33cbf3 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,15 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +media/cocowb/9fcab59ec353483a0635_gt.jpg filter=lfs diff=lfs merge=lfs -text +media/cocowb/9fcab59ec353483a0635_rgb.jpg filter=lfs diff=lfs merge=lfs -text +media/genhumanego/team_gen-humanego_0_gt.jpg filter=lfs diff=lfs merge=lfs -text +media/genhumanego/team_gen-humanego_0_rgb.jpg filter=lfs diff=lfs merge=lfs -text +media/genhumanego/team_gen-humanego_1_gt.jpg filter=lfs diff=lfs merge=lfs -text +media/genhumanego/team_gen-humanego_1_rgb.jpg filter=lfs diff=lfs merge=lfs -text +media/genhumanego/team_gen-humanego_2_gt.jpg filter=lfs diff=lfs merge=lfs -text +media/genhumanego/team_gen-humanego_2_rgb.jpg filter=lfs diff=lfs merge=lfs -text +media/genhumanego/team_gen-humanego_3_gt.jpg filter=lfs diff=lfs merge=lfs -text +media/genhumanego/team_gen-humanego_3_rgb.jpg filter=lfs diff=lfs merge=lfs -text +media/mpiinzsl/mpiinzsl_047704994_01_r_native2d_gt.jpg filter=lfs diff=lfs merge=lfs -text +media/mpiinzsl/mpiinzsl_047704994_01_r_native2d_rgb.jpg filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md index 6bd44ddbc5b9d726b9b8ac74b6dbffb31554f46d..94f7dd8999670f84eb839ae0f9dff621f150d6e0 100644 --- a/README.md +++ b/README.md @@ -33,6 +33,9 @@ data/previews.json # 样例登记 media// # 样例图片 tools/make_data.py # 编辑数据集信息的地方 tools/add_preview.py # 添加一对 RGB / GT 叠加样例 +tools/import_team_previews.py # 从团队 Space 导入已有叠加样例 +tools/server_sync.py # 在数据服务器上批量生成样例，传到 HF Dataset 仓库并登记 +tools/render_overlay.py # 把 MANO / SOMA 网格投影到 RGB 上生成叠加图 tools/build.py # 由 src/page.html 生成 index.html ``` @@ -46,6 +49,13 @@ vim tools/make_data.py && python tools/make_data.py python tools/add_preview.py hograspnet --rgb s01_rgb.png --gt s01_gt.png \ --caption "受试者 01，侧捏" --source "官方 MANO → SOMA" +# 在数据服务器上批量同步样例（推荐，见 tools/server_sync.py 顶部说明） +export HF_TOKEN=hf_xxx +python tools/server_sync.py manifest.jsonl --replace + +# 从团队图册同步已有样例 +python tools/import_team_previews.py --per 4 + # 改了页面源码后 python tools/build.py diff --git a/data/datasets.json b/data/datasets.json index 695109fd56704262e79e1d729ff8c44c580bb992..7dcafdad3b1f6b66969410f2d988a4c0ddec7970 100644 --- a/data/datasets.json +++ b/data/datasets.json @@ -1012,6 +1012,7 @@ "depth": true }, "fills": "手与表面接触的压力，带第一视角。", + "risk": "HaMeR 初始化后用多视角 RGB-D、分割、压力和时序约束拟合 MANO，手上没有 marker。", "links": [ { "label": "论文 arXiv:2409.02224", @@ -1024,7 +1025,6 @@ "url": "https://yangzijing-nine-source-hand-data-browser.static.hf.space/egocentric_embodied_datasets_20261020/index.html" } ], - "risk": "", "subjects": "", "scale": "" }, @@ -1038,15 +1038,14 @@ ], "source": "ego", "method": "marker", - "gt": "手部 3D（光学动捕）", + "gt": "MANO（光捕 marker 经 markers2mano 拟合）", "tier": "S2", "route": "R1", - "camera": "第一视角 + 26 相机光学动捕", + "camera": "头戴 GoPro 60 FPS + 26 相机光学动捕", "rig": { "ego": true }, "fills": "第一视角与肌电同步，光学动捕真值。", - "risk": "手部模型格式待核。", "links": [ { "label": "论文 arXiv:2605.05712", @@ -1059,6 +1058,7 @@ "url": "https://yangzijing-nine-source-hand-data-browser.static.hf.space/egocentric_embodied_datasets_20261020/index.html" } ], + "risk": "", "subjects": "", "scale": "" }, @@ -1075,14 +1075,14 @@ "gt": "MANO", "tier": "S3", "route": "R1", - "camera": "多台 RGB-D + 头显第一视角", + "camera": "多台 RGB-D + HoloLens 第一视角 RGB", "rig": { "ego": true, "exo": 8, "depth": true }, "fills": "手物交互，含第一视角。", - "risk": "标注细节待核。", + "risk": "多视角 RGB-D 与关键点约束拟合 MANO，不使用光捕。", "team": [ { "label": "团队图册：第一人称具身数据集", @@ -1112,7 +1112,7 @@ "exo": 12 }, "fills": "双手使用工具的交互。", - "risk": "标注方式待核，暂归入关键点三角化。", + "risk": "多视角 MMPose 三角化后做 MANO 接触与时序拟合，手上没有光捕。", "team": [ { "label": "团队图册：第一人称具身数据集", @@ -1720,10 +1720,10 @@ "第一人称图册" ], "source": "norgb", - "method": "pseudo", - "gt": "自动标注为主", - "tier": "S5", - "route": "R7", + "method": "mvkp", + "gt": "UmeTrack（另提供 MANO / MHR 转换）", + "tier": "S4", + "route": "R1", "camera": "黑白鱼眼", "rig": { "ego": true, @@ -1731,7 +1731,7 @@ "mono": true }, "fills": "团队已排除。", - "risk": "主标注自动生成，人工核验子集不具代表性；黑白鱼眼不满足真实 RGB 条件。", + "risk": "无标记多视角自动拟合，人工核验子集不具代表性；黑白鱼眼不满足真实 RGB 条件。", "team": [ { "label": "团队图册：第一人称具身数据集", diff --git a/data/previews.json b/data/previews.json index 0967ef424bce6791893e9a57bb952f80fd536e93..8d83c4b188d025344dfef0cc7ddcc3f61667e411 100644 --- a/data/previews.json +++ b/data/previews.json @@ -1 +1,652 @@ -{} +{ + "egodex": [ + { + "rgb": "media/egodex/team_egodex_0_rgb.jpg", + "gt": "media/egodex/team_egodex_0_gt.jpg", + "caption": "test/stock_unstock_fridge/1 / frame 201 · Vision Pro RGB · Apple EgoDex · 来源 CC-BY-NC-ND 4.0 · 复用已有研究对照画面", + "source": "RGB + 官方 ARKit 3D 骨架投影（估计标签）（团队图册 2026-10-22）" + }, + { + "rgb": "media/egodex/team_egodex_1_rgb.jpg", + "gt": "media/egodex/team_egodex_1_gt.jpg", + "caption": "test/stock_unstock_fridge/1 / frame 321 · Vision Pro RGB · Apple EgoDex · 来源 CC-BY-NC-ND 4.0 · 复用已有研究对照画面", + "source": "RGB + 官方 ARKit 3D 骨架投影（估计标签）（团队图册 2026-10-22）" + }, + { + "rgb": "media/egodex/team_egodex_2_rgb.jpg", + "gt": "media/egodex/team_egodex_2_gt.jpg", + "caption": "test/braid_unbraid/3 / frame 413 · Vision Pro RGB · Apple EgoDex · 来源 CC-BY-NC-ND 4.0 · 复用已有研究对照画面", + "source": "RGB + 官方 ARKit 3D 骨架投影（估计标签）（团队图册 2026-10-22）" + }, + { + "rgb": "media/egodex/team_egodex_3_rgb.jpg", + "gt": "media/egodex/team_egodex_3_gt.jpg", + "caption": "test/braid_unbraid/3 / frame 533 · Vision Pro RGB · Apple EgoDex · 来源 CC-BY-NC-ND 4.0 · 复用已有研究对照画面", + "source": "RGB + 官方 ARKit 3D 骨架投影（估计标签）（团队图册 2026-10-22）" + } + ], + "egoemg": [ + { + "rgb": "media/egoemg/team_egoemg_0_rgb.jpg", + "gt": "media/egoemg/team_egoemg_0_gt.jpg", + "caption": "episode_000028 · frame 600 · 10.00 s · 头戴 GoPro · 60 FPS · CC-BY-NC-4.0（论文 A.3）；作者公开旧版镜像；本页作抽帧、缩放和 MANO 叠加", + "source": "RGB + 光捕重建 MANO（团队图册 2026-10-22）" + }, + { + "rgb": "media/egoemg/team_egoemg_1_rgb.jpg", + "gt": "media/egoemg/team_egoemg_1_gt.jpg", + "caption": "episode_000000 · frame 25139 · 418.98 s · gesture ID 40 · 头戴 GoPro · 60 FPS · CC-BY-NC-4.0（论文 A.3）；Ziheng Xi et al.；作者公开旧版镜像；抽帧、缩放和 MANO 叠加", + "source": "RGB + 光捕重建 MANO（团队图册 2026-10-22）" + }, + { + "rgb": "media/egoemg/team_egoemg_2_rgb.jpg", + "gt": "media/egoemg/team_egoemg_2_gt.jpg", + "caption": "episode_000023 · frame 41250 · 687.50 s · gesture ID 12 · 头戴 GoPro · 60 FPS · CC-BY-NC-4.0（论文 A.3）；Ziheng Xi et al.；作者公开旧版镜像；抽帧、缩放和 MANO 叠加", + "source": "RGB + 光捕重建 MANO（团队图册 2026-10-22）" + }, + { + "rgb": "media/egoemg/team_egoemg_3_rgb.jpg", + "gt": "media/egoemg/team_egoemg_3_gt.jpg", + "caption": "episode_000037 · frame 50260 · 837.67 s · gesture ID 59 · 手指姿态不贴合（原因未确认） · 头戴 GoPro · 60 FPS · CC-BY-NC-4.0（论文 A.3）；Ziheng Xi et al.；作者公开旧版镜像；抽帧、缩放和 MANO 叠加", + "source": "RGB + 光捕重建 MANO（团队图册 2026-10-22）" + } + ], + "egopressure": [ + { + "rgb": "media/egopressure/team_egopressure_0_rgb.jpg", + "gt": "media/egopressure/team_egopressure_0_gt.jpg", + "caption": "p_001_index_press_low_x5_left · frame 28 · cam-d · 头戴 RGB-D 相机 · CC-BY-NC-SA-4.0；Zhao, Kwon, Streli, Pollefeys, Holz；MANO: Romero et al.；仅渲染图", + "source": "RGB + 官方拟合 MANO 网格（团队图册 2026-10-22）" + }, + { + "rgb": "media/egopressure/team_egopressure_1_rgb.jpg", + "gt": "media/egopressure/team_egopressure_1_gt.jpg", + "caption": "p_002_pinch_thumb-down_high_5x_left · frame 248 · cam-d · 头戴 RGB-D 相机 · CC-BY-NC-SA-4.0；Zhao, Kwon, Streli, Pollefeys, Holz；MANO: Romero et al.；仅渲染图", + "source": "RGB + 官方拟合 MANO 网格（团队图册 2026-10-22）" + }, + { + "rgb": "media/egopressure/team_egopressure_2_rgb.jpg", + "gt": "media/egopressure/team_egopressure_2_gt.jpg", + "caption": "p_003_press_fingers_high_5x_right · frame 115 · cam-d · 头戴 RGB-D 相机 · CC-BY-NC-SA-4.0；Zhao, Kwon, Streli, Pollefeys, Holz；MANO: Romero et al.；仅渲染图", + "source": "RGB + 官方拟合 MANO 网格（团队图册 2026-10-22）" + }, + { + "rgb": "media/egopressure/team_egopressure_3_rgb.jpg", + "gt": "media/egopressure/team_egopressure_3_gt.jpg", + "caption": "p_004_type_ipad_5x_right · frame 291 · cam-d · 头戴 RGB-D 相机 · CC-BY-NC-SA-4.0；Zhao, Kwon, Streli, Pollefeys, Holz；MANO: Romero et al.；仅渲染图", + "source": "RGB + 官方拟合 MANO 网格（团队图册 2026-10-22）" + } + ], + "egoverse": [ + { + "rgb": "media/egoverse/team_egoverse_0_rgb.jpg", + "gt": "media/egoverse/team_egoverse_0_gt.jpg", + "caption": "2025-09-20-18-44-29-000000 / frame 60 · Aria · front_1 · CC BY-SA 4.0 · RL2 / EgoVerse", + "source": "RGB + 官方 Aria 3D 关键点投影（估计标签）（团队图册 2026-10-22）" + }, + { + "rgb": "media/egoverse/team_egoverse_1_rgb.jpg", + "gt": "media/egoverse/team_egoverse_1_gt.jpg", + "caption": "2025-09-20-18-44-29-000000 / frame 150 · Aria · front_1 · CC BY-SA 4.0 · RL2 / EgoVerse", + "source": "RGB + 官方 Aria 3D 关键点投影（估计标签）（团队图册 2026-10-22）" + }, + { + "rgb": "media/egoverse/team_egoverse_2_rgb.jpg", + "gt": "media/egoverse/team_egoverse_2_gt.jpg", + "caption": "2025-09-20-18-44-29-000000 / frame 300 · Aria · front_1 · CC BY-SA 4.0 · RL2 / EgoVerse", + "source": "RGB + 官方 Aria 3D 关键点投影（估计标签）（团队图册 2026-10-22）" + }, + { + "rgb": "media/egoverse/team_egoverse_3_rgb.jpg", + "gt": "media/egoverse/team_egoverse_3_gt.jpg", + "caption": "2025-09-20-18-44-29-000000 / frame 600 · Aria · front_1 · CC BY-SA 4.0 · RL2 / EgoVerse", + "source": "RGB + 官方 Aria 3D 关键点投影（估计标签）（团队图册 2026-10-22）" + } + ], + "hocap": [ + { + "rgb": "media/hocap/team_hocap_0_rgb.jpg", + "gt": "media/hocap/team_hocap_0_gt.jpg", + "caption": "subject_7-20231022_192832 · frame 45 · HoloLens 第一视角 RGB · CC-BY-4.0；署名 Jikai Wang 等；已抽帧并叠加", + "source": "RGB + 官方 MANO 拟合标注（团队图册 2026-10-22）" + }, + { + "rgb": "media/hocap/team_hocap_1_rgb.jpg", + "gt": "media/hocap/team_hocap_1_gt.jpg", + "caption": "subject_7-20231022_192832 · frame 175 · HoloLens 第一视角 RGB · CC-BY-4.0；署名 Jikai Wang 等；已抽帧并叠加", + "source": "RGB + 官方 MANO 拟合标注（团队图册 2026-10-22）" + }, + { + "rgb": "media/hocap/team_hocap_2_rgb.jpg", + "gt": "media/hocap/team_hocap_2_gt.jpg", + "caption": "subject_7-20231022_192832 · frame 240 · HoloLens 第一视角 RGB · CC-BY-4.0；署名 Jikai Wang 等；已抽帧并叠加", + "source": "RGB + 官方 MANO 拟合标注（团队图册 2026-10-22）" + }, + { + "rgb": "media/hocap/team_hocap_3_rgb.jpg", + "gt": "media/hocap/team_hocap_3_gt.jpg", + "caption": "subject_7-20231022_192832 · frame 370 · HoloLens 第一视角 RGB · CC-BY-4.0；署名 Jikai Wang 等；已抽帧并叠加", + "source": "RGB + 官方 MANO 拟合标注（团队图册 2026-10-22）" + } + ], + "interhand": [ + { + "rgb": "media/interhand/interhand_test_C1_f27268_right_cam400375_rgb.jpg", + "gt": "media/interhand/interhand_test_C1_f27268_right_cam400375_gt.jpg", + "caption": "ROM07_Rt_Finger_Occlusions · 帧 27268 · 右手 · test", + "source": "MANO（团队图册 2026-10-13）" + }, + { + "rgb": "media/interhand/interhand_test_C1_f27502_right_cam400375_rgb.jpg", + "gt": "media/interhand/interhand_test_C1_f27502_right_cam400375_gt.jpg", + "caption": "ROM07_Rt_Finger_Occlusions · 帧 27502 · 右手 · test", + "source": "MANO（团队图册 2026-10-13）" + }, + { + "rgb": "media/interhand/interhand_test_C1_f28108_right_cam400285_rgb.jpg", + "gt": "media/interhand/interhand_test_C1_f28108_right_cam400285_gt.jpg", + "caption": "ROM07_Rt_Finger_Occlusions · 帧 28108 · 右手 · test", + "source": "MANO（团队图册 2026-10-13）" + }, + { + "rgb": "media/interhand/interhand_test_C1_f27376_right_cam400285_rgb.jpg", + "gt": "media/interhand/interhand_test_C1_f27376_right_cam400285_gt.jpg", + "caption": "ROM07_Rt_Finger_Occlusions · 帧 27376 · 右手 · test", + "source": "MANO（团队图册 2026-10-13）" + } + ], + "mpiinzsl": [ + { + "rgb": "media/mpiinzsl/mpiinzsl_025270786_01_l_native2d_rgb.jpg", + "gt": "media/mpiinzsl/mpiinzsl_025270786_01_l_native2d_gt.jpg", + "caption": "静态图像 · 帧 025270786.jpg · 左手 · manual_train", + "source": "2D（团队图册 2026-10-13）" + }, + { + "rgb": "media/mpiinzsl/mpiinzsl_047704994_01_r_native2d_rgb.jpg", + "gt": "media/mpiinzsl/mpiinzsl_047704994_01_r_native2d_gt.jpg", + "caption": "静态图像 · 帧 047704994.jpg · 右手 · manual_train", + "source": "2D（团队图册 2026-10-13）" + }, + { + "rgb": "media/mpiinzsl/mpiinzsl_Ricki_unit_8_flv_000040_l_native2d_rgb.jpg", + "gt": "media/mpiinzsl/mpiinzsl_Ricki_unit_8_flv_000040_l_native2d_gt.jpg", + "caption": "静态图像 · 帧 Ricki_unit_8.flv_000040 · 左手 · manual_train", + "source": "2D（团队图册 2026-10-13）" + }, + { + "rgb": "media/mpiinzsl/mpiinzsl_Jackie_unit_10_flv_000118_r_native2d_rgb.jpg", + "gt": "media/mpiinzsl/mpiinzsl_Jackie_unit_10_flv_000118_r_native2d_gt.jpg", + "caption": "静态图像 · 帧 Jackie_unit_10.flv_000118 · 右手 · manual_train", + "source": "2D（团队图册 2026-10-13）" + } + ], + "openaoe": [ + { + "rgb": "media/openaoe/team_openaoe_0_rgb.jpg", + "gt": "media/openaoe/team_openaoe_0_gt.jpg", + "caption": "aoe_20260131_193028_p000 · frame 400 · 第一视角 RGB（官方去畸变） · Open-AoE Dataset License；仅发布抽帧与叠加图", + "source": "RGB + 官方 MANO 拟合标注（团队图册 2026-10-22）" + }, + { + "rgb": "media/openaoe/team_openaoe_1_rgb.jpg", + "gt": "media/openaoe/team_openaoe_1_gt.jpg", + "caption": "aoe_20260131_193028_p000 · frame 1800 · 第一视角 RGB（官方去畸变） · Open-AoE Dataset License；仅发布抽帧与叠加图", + "source": "RGB + 官方 MANO 拟合标注（团队图册 2026-10-22）" + }, + { + "rgb": "media/openaoe/team_openaoe_2_rgb.jpg", + "gt": "media/openaoe/team_openaoe_2_gt.jpg", + "caption": "aoe_20260131_193028_p000 · frame 2600 · 第一视角 RGB（官方去畸变） · Open-AoE Dataset License；仅发布抽帧与叠加图", + "source": "RGB + 官方 MANO 拟合标注（团队图册 2026-10-22）" + }, + { + "rgb": "media/openaoe/team_openaoe_3_rgb.jpg", + "gt": "media/openaoe/team_openaoe_3_gt.jpg", + "caption": "aoe_20260131_193028_p000 · frame 4300 · 第一视角 RGB（官方去畸变） · Open-AoE Dataset License；仅发布抽帧与叠加图", + "source": "RGB + 官方 MANO 拟合标注（团队图册 2026-10-22）" + } + ], + "show3d": [ + { + "rgb": "media/show3d/team_show3d_0_rgb.jpg", + "gt": "media/show3d/team_show3d_0_gt.jpg", + "caption": "ASC023/aria_inspecting_f5e9 · frame 100 · headset0 · CC-BY-NC-4.0；缩放/裁出对应相机", + "source": "原图 + 官方 MANO mesh（团队图册 2026-10-22）" + }, + { + "rgb": "media/show3d/team_show3d_1_rgb.jpg", + "gt": "media/show3d/team_show3d_1_gt.jpg", + "caption": "LSU826/cancoke_opening_d68c · frame 1492 · headset0 · 单色 ego · CC-BY-NC-4.0；Meta SHOW3D；裁出对应相机、缩放", + "source": "原图 + 官方 MANO mesh（团队图册 2026-10-22）" + }, + { + "rgb": "media/show3d/team_show3d_2_rgb.jpg", + "gt": "media/show3d/team_show3d_2_gt.jpg", + "caption": "HLU829/keyboard2_clean-keyboard_1d3c · frame 2088 · headset1 · 单色 ego · CC-BY-NC-4.0；Meta SHOW3D；裁出对应相机、缩放", + "source": "原图 + 官方 MANO mesh（团队图册 2026-10-22）" + }, + { + "rgb": "media/show3d/team_show3d_3_rgb.jpg", + "gt": "media/show3d/team_show3d_3_gt.jpg", + "caption": "PRI519/balandabowl_covering-uncovering_1178 · frame 2386 · headset1 · 单色 ego · CC-BY-NC-4.0；Meta SHOW3D；裁出对应相机、缩放", + "source": "原图 + 官方 MANO mesh（团队图册 2026-10-22）" + } + ], + "taco": [ + { + "rgb": "media/taco/team_taco_0_rgb.jpg", + "gt": 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"media/mtc/mtc_171204_pose2_f00028165_p5_right_3_gt.jpg", + "caption": "171204_pose2 · 帧 28165 · 右手 · training_data", + "source": "3D → 2D（团队图册 2026-10-13）" + }, + { + "rgb": "media/mtc/mtc_171204_pose2_f00028165_p5_right_12_rgb.jpg", + "gt": "media/mtc/mtc_171204_pose2_f00028165_p5_right_12_gt.jpg", + "caption": "171204_pose2 · 帧 28165 · 右手 · training_data", + "source": "3D → 2D（团队图册 2026-10-13）" + } + ], + "reinterhand": [ + { + "rgb": "media/reinterhand/reinter_train_c0_f175271_right_400503_rgb.jpg", + "gt": "media/reinterhand/reinter_train_c0_f175271_right_400503_gt.jpg", + "caption": "m--20210701--1058--0000000--pilot--relightablehandsy--participant0--two-hands · 帧 175271 · 右手 · train", + "source": "3D → 2D（团队图册 2026-10-13）" + }, + { + "rgb": "media/reinterhand/reinter_train_c0_f202212_right_400503_rgb.jpg", + "gt": "media/reinterhand/reinter_train_c0_f202212_right_400503_gt.jpg", + "caption": "m--20210701--1058--0000000--pilot--relightablehandsy--participant0--two-hands · 帧 202212 · 右手 · train", + "source": "3D → 2D（团队图册 2026-10-13）" + }, + { + "rgb": "media/reinterhand/reinter_train_c0_f292944_right_400503_rgb.jpg", + "gt": "media/reinterhand/reinter_train_c0_f292944_right_400503_gt.jpg", + "caption": "m--20210701--1058--0000000--pilot--relightablehandsy--participant0--two-hands · 帧 292944 · 右手 · train", + "source": "3D → 2D（团队图册 2026-10-13）" + }, + { + "rgb": "media/reinterhand/reinter_train_c0_f303030_right_400503_rgb.jpg", + "gt": "media/reinterhand/reinter_train_c0_f303030_right_400503_gt.jpg", + "caption": "m--20210701--1058--0000000--pilot--relightablehandsy--participant0--two-hands · 帧 303030 · 右手 · train", + "source": "3D → 2D（团队图册 2026-10-13）" + } + ], + "rhd": [ + { + "rgb": "media/rhd/5807bcc820cac79abd07_rgb.jpg", + "gt": "media/rhd/5807bcc820cac79abd07_gt.jpg", + "caption": "image_30087 · 帧 30087 · 左手 · official_v1.1_training_proxy", + "source": "3D → 2D（团队图册 2026-10-13）" + }, + { + "rgb": "media/rhd/0941a85c7723f23e66bc_rgb.jpg", + "gt": "media/rhd/0941a85c7723f23e66bc_gt.jpg", + "caption": "image_33342 · 帧 33342 · 右手 · official_v1.1_training_proxy", + "source": "3D → 2D（团队图册 2026-10-13）" + }, + { + "rgb": "media/rhd/a8db512d6c52ae380d21_rgb.jpg", + "gt": "media/rhd/a8db512d6c52ae380d21_gt.jpg", + "caption": "image_13893 · 帧 13893 · 左手 · official_v1.1_training_proxy", + "source": "3D → 2D（团队图册 2026-10-13）" + }, + { + "rgb": "media/rhd/14514fa31d01d7d4624b_rgb.jpg", + "gt": "media/rhd/14514fa31d01d7d4624b_gt.jpg", + "caption": "image_29917 · 帧 29917 · 左手 · official_v1.1_training_proxy", + "source": "3D → 2D（团队图册 2026-10-13）" + } + ] +} diff --git a/index.html b/index.html index dc10eeb1fba2728d3872da57b8d936b9a1a20e1f..4a888ec9cf15264c292214bbd35513a036c9b56f 100644 --- a/index.html +++ b/index.html @@ -10,7 +10,7 @@ @@ -229,10 +248,10 @@ footer { font-size: 0.8rem; color: var(--muted); border-top: 1px solid var(--lin
SOMA 手部数据图谱
@@ -246,17 +265,24 @@ footer { font-size: 0.8rem; color: var(--muted); border-top: 1px solid var(--lin "use strict"; var DATA = null, PREV = {}, BYID = {}, M = {}; var app = document.getElementById("app"); - var filt = { source: "", method: "", tier: "", status: "", q: "" }; + var filt = { source: "", method: "", tier: "", q: "", hasPrev: false }; + var ORDER = ["own", "p0", "p1", "p2", "used", "surveyed", "no"]; + var TIER_OF = { scan: "S1", marker: "S2", rgbd: "S3", mvkp: "S4", device: "-", synth: "SY", manual2d: "-", pseudo: "S5" }; function esc(s) { return String(s == null ? "" : s).replace(/[&<>"']/g, function (c) { return { "&": "&", "<": "<", ">": ">", '"': """, "'": "'" }[c]; }); } function by(list) { var o = {}; list.forEach(function (x) { o[x.id] = x; }); return o; } - function stChip(id) { var s = M.status[id]; return '' + esc(s.name) + ""; } - function tierChip(id, title) { var t = M.tier[id]; return '' + esc(id === "-" ? "—" : id) + ""; } - function dsLink(d, cls) { return '' + esc(d.name) + ""; } - function statusOrder(s) { return ["own", "p0", "p1", "p2", "used", "surveyed", "no"].indexOf(s); } - function sorted(list) { return list.slice().sort(function (a, b) { return statusOrder(a.status) - statusOrder(b.status) || a.name.localeCompare(b.name); }); } + function stChip(id) { return '' + esc(M.status[id].name) + ""; } + function tierChip(id) { var t = M.tier[id]; return '' + (id === "-" ? "—" : esc(id)) + ""; } + function nPrev(d) { return (PREV[d.id] || []).length; } + function dsLink(d, showN) { var n = nPrev(d); return '' + esc(d.name) + (showN && n ? ' ' + n + "" : "") + ""; } + function sorted(list) { return list.slice().sort(function (a, b) { return ORDER.indexOf(a.status) - ORDER.indexOf(b.status) || a.name.localeCompare(b.name); }); } + function partHead(title, note) { return '

' + esc(title) + "

" + (note ? "

" + note + "

" : "") + "
"; } + function thumb(d) { + var p = (PREV[d.id] || [])[0]; + return p ? '' : '暂无样例'; + } - /* ---------- 采集装置示意图 ---------- */ + /* ---------- 采集装置示意 ---------- */ function rigSvg(d) { var r = d.rig || {}, W = 320, H = 200, cx = 160, cy = 118, out = []; out.push(''); @@ -288,7 +314,6 @@ footer { font-size: 0.8rem; color: var(--muted); border-top: 1px solid var(--lin out.push(''); out.push('' + (r.pseudoEgo ? "固定于眼高（模拟第一视角）" : (r.fisheye ? "第一视角 · 宽视场" : "第一视角")) + (r.mono ? " · 黑白" : "") + ""); } - // 手 var hx2 = cx, hy2 = cy + 10; out.push(''); out.push(''); @@ -301,194 +326,191 @@ footer { font-size: 0.8rem; color: var(--muted); border-top: 1px solid var(--lin out.push(''); out.push('无 RGB 图像'); } - if (r.synth) out.push('渲染生成'); - if (r.wild && !r.ego) out.push('网络 / 日常图像'); - if (r.scanner) out.push('扫描仪多相机环'); + var foot = r.synth ? "渲染生成" : (r.wild && !r.ego) ? "网络 / 日常图像" : r.scanner ? "扫描仪多相机环" : ""; + if (foot) out.push('' + foot + ""); out.push(""); return out.join(""); } + function flow(steps) { return '
' + steps.map(function (s, i) { return (i ? '' : "") + '' + esc(s) + ""; }).join("") + "
"; } - function flow(steps) { - return '
' + steps.map(function (s, i) { return (i ? '' : "") + '' + esc(s) + ""; }).join("") + "
"; - } - - /* ---------- 视图：总览 ---------- */ + /* ---------- 总览 ---------- */ function vOverview() { var ds = DATA.datasets, cnt = {}; ds.forEach(function (d) { cnt[d.status] = (cnt[d.status] || 0) + 1; }); - var h = []; - h.push('

按数据来源和标注方法看手部数据

' + esc(DATA.meta.goal) + " 矩阵的行按 shape 真值的可信度从高到低排列，列是数据来源；点任一数据集看详情、采集示意和样例。

"); - h.push('
' + DATA.meta.statuses.map(function (s) { return "
" + stChip(s.id) + '' + (cnt[s.id] || 0) + "
"; }).join("") + '
共 ' + ds.length + " 个
"); - h.push('
'); + var withPrev = ds.filter(function (d) { return nPrev(d); }).length; + var h = ['
']; + h.push('

手部数据集，按来源和标注方法分类

' + esc(DATA.meta.goal) + "

"); + h.push('
' + ds.length + '个数据集
' + withPrev + '个有样例
' + ORDER.map(function (s) { return "
" + stChip(s) + "" + (cnt[s] || 0) + "
"; }).join("") + "
"); + + h.push('
' + partHead("分布矩阵", "行是标注方法，按 shape 真值可信度从高到低；列是数据来源。点名称看详情。")); + h.push('
标注方法 ＼ 数据来源
'); DATA.meta.sources.forEach(function (s) { h.push(""); }); h.push(""); - var tierOf = { scan: "S1", marker: "S2", rgbd: "S3", mvkp: "S4", device: "-", synth: "SY", manual2d: "-", pseudo: "S5" }; DATA.meta.methods.forEach(function (m) { - h.push('"); + h.push('"); DATA.meta.sources.forEach(function (s) { var list = sorted(ds.filter(function (d) { return d.method === m.id && d.source === s.id; })); - h.push(""); + h.push(""); }); h.push(""); }); - h.push("
标注方法" + esc(s.name) + "" + esc(s.desc) + "
' + esc(m.name) + "
" + tierChip(tierOf[m.id]) + "
' + esc(m.name) + "" + tierChip(TIER_OF[m.id]) + "" + (list.length ? '
' + list.map(function (d) { return dsLink(d); }).join("") + "
" : '·') + "		" + (list.length ? '
' + list.map(function (d) { return dsLink(d); }).join("") + "
" : '·') + "
"); - h.push('
标签颜色表示状态：' + DATA.meta.statuses.map(function (s) { return "" + stChip(s.id) + esc(s.desc) + ""; }).join("") + "
"); + h.push('
状态：' + ORDER.map(stChip).join("") + "
"); - h.push('

shape 真值分层

大部分数据集的 β 是从图像或关键点拟合的。监督 SOMA shape 时只用 S1 到 S3 和合成数据，S4 当弱监督，S5 和“—”不参与 shape。

'); - DATA.meta.tiers.forEach(function (t) { h.push("
" + tierChip(t.id) + "" + esc(t.name) + '' + esc(t.desc) + "
"); }); - h.push("
"); var p0 = ds.filter(function (d) { return d.status === "p0"; }); - h.push('

建议先接入

    '); - p0.forEach(function (d) { h.push('
  1. ' + esc(d.name) + "　" + esc(d.fills) + "
    2. "); }); - h.push('

完整理由和其余新增项见数据集目录，状态筛选选「P0 新增」到「P2 新增」。

'); - h.push("
"); + h.push('
' + partHead("建议先接入") + '
'); + p0.forEach(function (d) { h.push('' + stChip(d.status) + "" + esc(d.name) + '' + esc(d.fills) + ""); }); + h.push("
"); + h.push('
' + partHead("shape 真值分层", "只有 S1 到 S3 和合成数据用来监督 SOMA shape。") + '
'); + DATA.meta.tiers.forEach(function (t) { h.push("
" + tierChip(t.id) + "" + esc(t.name) + '' + esc(t.desc) + "
"); }); + h.push("
"); return h.join(""); } - /* ---------- 视图：目录 ---------- */ + /* ---------- 数据集目录 ---------- */ function opts(list, cur, all) { return '" + list.map(function (x) { return '"; }).join(""); } function vCatalog() { - var h = []; - h.push('

数据集目录

按数据来源、标注方法、shape 层级和状态筛选。每行是一个数据集，点开看完整信息。

'); - h.push(''); - h.push('

'); + var h = ['

数据集

按状态分组。缩略图是 GT 叠加样例，点开看完整信息。

']; + h.push('

'); + h.push('
'); return h.join(""); } function renderRows() { var q = filt.q.trim().toLowerCase(); - var list = sorted(DATA.datasets.filter(function (d) { + var list = DATA.datasets.filter(function (d) { if (filt.source && d.source !== filt.source) return false; if (filt.method && d.method !== filt.method) return false; if (filt.tier && d.tier !== filt.tier) return false; - if (filt.status && d.status !== filt.status) return false; + if (filt.hasPrev && !nPrev(d)) return false; if (q) { var hay = [d.name, d.camera, d.gt, d.fills, d.risk, d.scale, d.subjects, (d.tags || []).join(" ")].join(" ").toLowerCase(); if (hay.indexOf(q) < 0) return false; } return true; - })); - document.getElementById("cnt").textContent = "显示 " + list.length + " / " + DATA.datasets.length + " 个数据集"; - var h = ['
数据集数据来源标注方法真值shape规模状态
']; - if (!list.length) h.push('
没有符合条件的数据集。换一个筛选条件，或点「清除筛选」。
'); - list.forEach(function (d) { - h.push(''); - h.push('' + esc(d.name) + "" + esc(d.year) + ""); - h.push('数据来源' + esc(M.source[d.source].name) + ""); - h.push('标注方法' + esc(M.method[d.method].name) + ""); - h.push('真值' + esc(d.gt) + ""); - h.push('shape' + tierChip(d.tier) + ""); - h.push('规模' + esc([d.subjects, d.scale].filter(Boolean).join("；") || "—") + ""); - h.push('' + stChip(d.status) + ""); - h.push(""); }); - document.getElementById("rows").innerHTML = h.join(""); + document.getElementById("cnt").textContent = "显示 " + list.length + " / " + DATA.datasets.length + " 个"; + var h = []; + if (!list.length) h.push('

没有符合条件的数据集。换个条件，或点「清除」。

'); + ORDER.forEach(function (st) { + var g = list.filter(function (d) { return d.status === st; }).sort(function (a, b) { return a.name.localeCompare(b.name); }); + if (!g.length) return; + h.push('
' + stChip(st) + '' + esc(M.status[st].desc) + " · " + g.length + ' 个
'); + g.forEach(function (d) { + h.push('' + thumb(d)); + h.push('' + esc(d.name) + '' + esc(d.year) + ""); + h.push('' + esc(M.source[d.source].name) + '' + esc(M.method[d.method].name) + ""); + h.push('' + tierChip(d.tier) + " " + esc(d.gt) + ""); + h.push('' + esc([d.subjects, d.scale].filter(Boolean).join("；") || "规模未列") + ""); + }); + h.push("
"); + }); + document.getElementById("groups").innerHTML = h.join(""); } function bindCatalog() { - ["source", "method", "tier", "status"].forEach(function (k) { - document.getElementById("f-" + k).addEventListener("change", function (e) { filt[k] = e.target.value; renderRows(); }); - }); + ["source", "method", "tier"].forEach(function (k) { document.getElementById("f-" + k).addEventListener("change", function (e) { filt[k] = e.target.value; renderRows(); }); }); document.getElementById("f-q").addEventListener("input", function (e) { filt.q = e.target.value; renderRows(); }); + document.getElementById("f-prev").addEventListener("change", function (e) { filt.hasPrev = e.target.checked; renderRows(); }); document.getElementById("ff").addEventListener("submit", function (e) { e.preventDefault(); }); - document.getElementById("f-reset").addEventListener("click", function () { filt = { source: "", method: "", tier: "", status: "", q: "" }; render(); }); + document.getElementById("f-reset").addEventListener("click", function () { filt = { source: "", method: "", tier: "", q: "", hasPrev: false }; render(); }); renderRows(); } - /* ---------- 视图：标注方法 ---------- */ + /* ---------- 详情 ---------- */ + function vDetail(id) { + var d = BYID[id]; + if (!d) return '

找不到数据集「' + esc(id) + '」。回到数据集。

'; + var m = M.method[d.method], s = M.source[d.source], rt = M.route[d.route], t = M.tier[d.tier]; + var all = sorted(DATA.datasets), idx = all.indexOf(d), prev = all[idx - 1], next = all[idx + 1]; + var h = ['

数据集 / ' + esc(M.status[d.status].name) + "

"]; + + h.push('

' + esc(d.name) + '

' + stChip(d.status) + (d.year ? '' + esc(d.year) + "" : "") + (d.tags || []).filter(function (x) { return x !== "新增" && x !== "不建议"; }).map(function (x) { return '' + esc(x) + ""; }).join("") + '

' + esc(d.fills) + "

"); + + h.push('
'); + h.push('
数据来源' + esc(s.name) + "
"); + h.push('
标注方法' + esc(m.name) + "
"); + h.push('
真值' + esc(d.gt) + "
"); + h.push('
shape 层级' + tierChip(d.tier) + " " + esc(t.name) + "
"); + h.push('
转到 SOMA' + esc(rt.id + " " + rt.name) + "" + (rt.shape ? "" : '
不监督 shape') + "
"); + h.push("
"); + + var pv = PREV[d.id] || []; + h.push('
' + partHead("样例", pv.length ? "左：原始 RGB；右：GT 叠加。" : "")); + if (pv.length) { + h.push('
'); + pv.forEach(function (p, i) { + h.push('
' + esc(d.name +
RGB
' + esc(d.name +
' + esc(p.source || "GT 叠加") + "
" + (p.caption ? '

' + esc(p.caption) + "

" : "") + "
"); + }); + h.push("
"); + } else { + h.push('

还没有样例。添加方法见样例页。

' + ((d.team || []).length ? "

团队已有可视化：" + d.team.map(function (x) { return '' + esc(x.label.replace(/^团队(图册|视频)：/, "")) + ""; }).join("，") + "

" : "") + "
"); + } + h.push("
"); + + h.push('

相机与规模

'); + h.push("
相机
" + esc(d.camera || "未列") + "
"); + h.push("
受试者
" + esc(d.subjects || "未列") + "
"); + h.push("
规模
" + esc(d.scale || "未列") + "
"); + if (d.risk) h.push('
注意

' + esc(d.risk) + "

"); + h.push('

采集与标注流程

' + rigSvg(d) + flow(m.steps) + '

按相机配置绘制的示意，不是真实画面；琥珀色圆点表示手上贴了光学 marker。

'); + + var links = (d.links || []).concat((d.team || []).map(function (x) { return { label: x.label, url: x.url }; })); + if (links.length) h.push('
' + partHead("链接") + '
"); + h.push('
' + (prev ? '← ' + esc(prev.name) + "" : "") + "" + (next ? '' + esc(next.name) + " →" : "") + "
"); + return h.join(""); + } + + /* ---------- 标注方法 ---------- */ function vMethods() { - var h = ['

标注方法

同一种标注方法得到的真值，可信度和适用范围相近。下面按 shape 真值可信度从高到低排列，每种方法给出流程、可信度、常见问题和转到 SOMA 的路线。

']; - var tierOf = { scan: "S1", marker: "S2", rgbd: "S3", mvkp: "S4", device: "-", synth: "SY", manual2d: "-", pseudo: "S5" }; + var h = ['

标注方法

按 shape 真值可信度从高到低排列。每种方法列出流程、可信度、常见问题、转到 SOMA 的路线和用到它的数据集。

']; + h.push('"); DATA.meta.methods.forEach(function (m) { var list = sorted(DATA.datasets.filter(function (d) { return d.method === m.id; })); - h.push('

' + esc(m.name) + "

" + tierChip(tierOf[m.id]) + '' + list.length + " 个数据集
"); - h.push(flow(m.steps)); - h.push('
可信度
' + esc(m.trust) + "
常见问题
" + esc(m.issues) + '
SOMA 路线
' + esc(m.route) + "
"); - h.push('
' + list.map(function (d) { return dsLink(d); }).join("") + "
"); + h.push('

' + esc(m.name) + "

" + tierChip(TIER_OF[m.id]) + '' + list.length + " 个数据集
" + flow(m.steps)); + h.push('
可信度
' + esc(m.trust) + "
常见问题
" + esc(m.issues) + '
转到 SOMA
' + esc(m.route) + '
' + list.map(function (d) { return dsLink(d); }).join("") + "
"); }); h.push("
"); return h.join(""); } - /* ---------- 视图：SOMA 转换 ---------- */ + /* ---------- 转到 SOMA ---------- */ function vSoma() { - var h = ['

转到 SOMA hand

没有公开数据集原生使用 SOMA 标注。SOMA-X 的 SOMAHandLayer 官方支持三种手部后端：SOMA 原生（20 维手型 identity）、MANO 和 MHR，许可为 Apache-2.0。转到 SOMA 拓扑之后，要监督原生 shape 还需要再拟合一次。

']; - h.push('
'); - h.push('

原始真值

  • 扫描网格
  • MANO 参数或网格
  • SMPL-X / SMPL-H
  • MHR 参数
  • 3D 关键点
  • 2D 关键点
'); - h.push('

映射到 SOMA 拓扑

  • SOMAHandLayer 的 MANO 后端
  • SOMAHandLayer 的 MHR 后端
  • SMPL-X 先取手部 MANO 部分
  • 关键点先定关节对应
'); - h.push('

SOMA 原生重拟合

  • 拟合 20 维 identity 和 pose
  • 按数据集统计顶点残差
  • 残差超阈值的帧不监督 shape
'); - h.push('

训练监督

  • shape：S1 到 S3、合成数据
  • pose：所有 3D 来源
  • 2D 重投影：全部带 2D 的来源
  • 伪标签只做弱监督
'); - h.push("
"); - h.push('

转换路线

'); + var h = ['

转到 SOMA hand

没有公开数据集原生使用 SOMA 标注。SOMA-X 的 SOMAHandLayer 官方支持 SOMA 原生、MANO、MHR 三种手部后端（Apache-2.0）。映射到 SOMA 拓扑后，要监督原生 shape 还需要再拟合一次。

']; + h.push('
' + partHead("四步流程") + '
'); + h.push('
1 · 原始真值
  • 扫描网格
  • MANO 参数或网格
  • SMPL-X / SMPL-H
  • MHR 参数
  • 3D / 2D 关键点
'); + h.push('
2 · 映射到 SOMA 拓扑
  • SOMAHandLayer 的 MANO 后端
  • SOMAHandLayer 的 MHR 后端
  • SMPL-X 先取手部
  • 关键点先定关节对应
'); + h.push('
3 · SOMA 原生重拟合
  • 拟合 20 维 identity 和 pose
  • 按数据集统计顶点残差
  • 残差超阈值不监督 shape
'); + h.push('
4 · 训练监督
  • shape：S1 到 S3、合成
  • pose：所有 3D 来源
  • 2D 重投影：带 2D 的来源
  • 伪标签只做弱监督
'); + h.push("
"); + h.push('
' + partHead("转换路线", "每张卡片对应一种原始真值格式。") + '
'); DATA.meta.routes.forEach(function (r) { - var n = DATA.datasets.filter(function (d) { return d.route === r.id; }).length; - h.push('
' + r.id + "

" + esc(r.name) + '

来源：' + esc(r.from) + "

" + esc(r.how) + '

' + (r.shape ? '可监督 shape' : '不监督 shape') + ' · ' + n + " 个数据集

"); + var list = DATA.datasets.filter(function (d) { return d.route === r.id; }); + h.push('
' + r.id + "

" + esc(r.name) + '

' + esc(r.how) + '

' + (r.shape ? '可监督 shape' : '不监督 shape') + ' · ' + list.length + " 个数据集

"); }); - h.push("
"); - h.push('

需要注意

    '); - h.push("
  • MANO 的 21 点里指尖取的是网格顶点，与 SOMA 关节定义不同，关键点类数据集必须先定对应表。
    • "); - h.push("
  • EgoRenderer 已经做过一次原始网格到 MANO 的 778 顶点拟合，再转 SOMA 是第二次转换，残差要分开记录。
    • "); - h.push("
  • shape 和尺度在单目里是耦合的；有内参时，shape 误差会表现为绝对尺度误差，评测时建议单独报标准姿态下的顶点误差和骨长误差。
    • "); + h.push("
"); + h.push('
' + partHead("注意事项") + '
    '); + h.push("
  • MANO 21 点的指尖是网格顶点，与 SOMA 关节定义不同；关键点类数据集要先定对应表。
    • "); + h.push("
  • EgoRenderer 已经做过一次原始网格到 MANO 的拟合，再转 SOMA 是第二次，残差分开记录。
    • "); + h.push("
  • 单目下 shape 和尺度耦合；评测单独报标准姿态下的顶点误差和骨长误差。
    • "); h.push("
  • SAM 3D Body 输出 MHR，可以走 R5 给野外图像生成伪标签；它的训练数据是否公开未查到。
    • "); - h.push('

参考

'); - h.push("
"); + h.push('
' + partHead("参考") + '
'); return h.join(""); } - /* ---------- 视图：样例素材 ---------- */ + /* ---------- 样例 ---------- */ function vSamples() { - var h = ['

样例素材

每个数据集的详情页会显示「RGB / GT 叠加」样例对。样例登记在 data/previews.json，图片放在 media/<数据集 id>/。用脚本添加，不需要改页面代码。

']; - h.push('

添加一对样例

python tools/add_preview.py hograspnet \\\n  --rgb  /data/hograspnet/s01_0042_rgb.png \\\n  --gt   /data/hograspnet/s01_0042_gt.png \\\n  --caption "受试者 01，侧捏，第一视角相机" \\\n  --source "官方 MANO，经 R1 转为 SOMA 后渲染"

脚本会把图片转成最长边 960 像素的 JPEG 复制到 media 目录，并追加到 previews.json。同名样例会被覆盖。

'); - h.push('

previews.json 格式

{\n  "hograspnet": [\n    {\n      "rgb": "media/hograspnet/s01_0042_rgb.jpg",\n      "gt": "media/hograspnet/s01_0042_gt.jpg",\n      "caption": "受试者 01，侧捏",\n      "source": "官方 MANO → SOMA"\n    }\n  ]\n}
'); - h.push('

当前样例覆盖

'); - sorted(DATA.datasets).forEach(function (d) { - var n = (PREV[d.id] || []).length; - h.push('"); - }); - h.push("
数据集状态本站样例团队已有可视化
' + esc(d.name) + "" + stChip(d.status) + '' + (n || "—") + "" + ((d.team || []).map(function (t) { return '' + esc(t.label.replace("团队图册：", "").replace("团队视频：", "视频：")) + ""; }).join("
") || '无') + "
"); - return h.join(""); - } - - /* ---------- 视图：详情 ---------- */ - function vDetail(id) { - var d = BYID[id]; - if (!d) return '

找不到数据集「' + esc(id) + '」。回到数据集目录。

'; - var m = M.method[d.method], s = M.source[d.source], rt = M.route[d.route], t = M.tier[d.tier]; - var all = sorted(DATA.datasets), idx = all.indexOf(d), prev = all[idx - 1], next = all[idx + 1]; - var h = ['

数据集目录 / ' + esc(s.name) + " / " + esc(m.name) + "

"]; - h.push('

' + esc(d.name) + '

' + stChip(d.status) + (d.tags || []).map(function (x) { return '' + esc(x) + ""; }).join("") + '' + esc(d.year) + "
"); - h.push('

基本信息

'); - h.push("
数据来源
" + esc(s.name) + '
' + esc(s.desc) + "
"); - h.push("
相机
" + esc(d.camera || "—") + "
"); - h.push('
标注方法
' + esc(m.name) + "
"); - h.push("
真值格式
" + esc(d.gt) + "
"); - h.push("
shape 层级
" + tierChip(d.tier) + " " + esc(t.name) + '，' + esc(t.desc) + "
"); - h.push('
SOMA 路线
' + esc(rt.id + " " + rt.name) + "" + (rt.shape ? "" : '，不监督 shape') + "
"); - h.push("
受试者
" + esc(d.subjects || "—") + "
"); - h.push("
规模
" + esc(d.scale || "—") + "
"); - h.push('
补什么

' + esc(d.fills) + "

"); - if (d.risk) h.push('
注意

' + esc(d.risk) + "

"); - h.push("
"); - h.push('

采集与标注示意

' + rigSvg(d) + flow(m.steps) + '

示意图按数据集的相机配置绘制，不是真实画面。琥珀色圆点表示手上贴有光学 marker。

'); - var pv = PREV[d.id] || []; - h.push('

样例：RGB 与 GT 叠加

'); - if (pv.length) { - h.push('
'); - pv.forEach(function (p, i) { - h.push('
' + esc(d.name +
RGB
' + esc(d.name +
GT 叠加' + (p.source ? "：" + esc(p.source) : "") + "
"); - if (p.caption) h.push('

' + esc(p.caption) + "

"); - }); - h.push("
"); - } else { - h.push('

本站还没有这个数据集的样例。用 tools/add_preview.py ' + esc(d.id) + " 添加，步骤见样例素材。

"); - if ((d.team || []).length) h.push("

团队已有的可视化：" + d.team.map(function (x) { return '' + esc(x.label) + ""; }).join("，") + "

"); - h.push("
"); - } - h.push("
"); - if ((d.links || []).length) h.push('

链接

"); - h.push('
' + (prev ? '← ' + esc(prev.name) + "" : "") + "" + (next ? '' + esc(next.name) + " →" : "") + "
"); - h.push("
"); + var ds = sorted(DATA.datasets), have = ds.filter(nPrev), none = ds.filter(function (d) { return !nPrev(d); }); + var pairs = have.reduce(function (a, d) { return a + nPrev(d); }, 0); + var h = ['

样例

每对样例是同一帧的原始 RGB 和 GT 叠加。图片放在独立的 HF Dataset 仓库或本站 media 目录，登记在 data/previews.json。

']; + h.push('
' + partHead("覆盖情况", have.length + " 个数据集共 " + pairs + " 对；" + none.length + " 个还没有。") ); + h.push('
已有样例（数字为对数）
' + have.map(function (d) { return dsLink(d, true); }).join("") + "
"); + h.push('
暂无样例
' + none.map(function (d) { return dsLink(d); }).join("") + "
"); + h.push('
' + partHead("怎么添加", "三种方式，按场景选一种。") + '
'); + h.push('

在数据所在的服务器上批量同步（推荐）

在存放数据集的机器上运行。图片传到独立的 HF Dataset 仓库并固定版本，Space 只登记 URL，网页不依赖 SSH。可以直接给已渲染好的叠加图，也可以给原图加网格（MANO 或 SOMA）由脚本渲染。

export HF_TOKEN=hf_xxx\npython tools/server_sync.py manifest.jsonl \\\n  --media-repo DarthSoluke/EgoNetDatasetResearch-media \\\n  --space DarthSoluke/EgoNetDatasetResearch
{"dataset": "hograspnet",\n "rgb": "/SSD/hograspnet/s01/0042.png",\n "mesh": "/SSD/hograspnet/s01/0042_soma.npz",\n "caption": "受试者 01，侧捏",\n "source": "官方 MANO → R1 → SOMA"}
'); + h.push('

从团队图册导入

复用 nine-source Space 里已经做好的叠加图，每个数据集取若干对。

python tools/import_team_previews.py --per 4
'); + h.push('

手动加一对

已有一张 RGB 和一张叠加图时用。

python tools/add_preview.py hograspnet \\\n  --rgb a_rgb.png --gt a_gt.png \\\n  --caption "说明" --source "GT 来源"
'); + h.push("
"); return h.join(""); } @@ -502,7 +524,7 @@ footer { font-size: 0.8rem; color: var(--muted); border-top: 1px solid var(--lin else if (hash === "soma") html = vSoma(); else if (hash === "samples") html = vSamples(); else { html = vOverview(); view = "overview"; } - app.innerHTML = html + '
数据整理于 ' + esc(DATA.meta.updated) + "。数值来自论文、项目页和团队已有图册；标为「待核」的字段尚未确认。修改数据请编辑 tools/make_data.py 后重新生成。
"; + app.innerHTML = html + '
整理于 ' + esc(DATA.meta.updated) + "。数值来自论文、项目页和团队图册；标「待核」的字段尚未确认。
"; document.querySelectorAll("nav.tabs a").forEach(function (a) { if (a.getAttribute("data-v") === view) a.setAttribute("aria-current", "page"); else a.removeAttribute("aria-current"); }); if (hash === "catalog") bindCatalog(); if (pendingJump) { var el = document.getElementById(pendingJump); pendingJump = null; if (el) { el.scrollIntoView(); return; } } @@ -525,7 +547,7 @@ footer { font-size: 0.8rem; color: var(--muted); border-top: 1px solid var(--lin fetch("data/datasets.json").then(function (r) { if (!r.ok) throw new Error(r.status); return r.json(); }), fetch("data/previews.json").then(function (r) { return r.ok ? r.json() : {}; }).catch(function () { return {}; }) ]).then(function (x) { boot(x[0], x[1]); }).catch(function () { - app.innerHTML = '

没能读取 data/datasets.json。直接双击打开 HTML 文件时浏览器会拦截读取，请用 python -m http.server 在本目录启动后访问，或部署到 Hugging Face Space。

'; + app.innerHTML = '

没能读取 data/datasets.json。直接双击打开 HTML 时浏览器会拦截读取，请在本目录运行 python -m http.server 后访问，或部署到 Hugging Face Space。

'; }); })(); diff --git a/media/arctic/6057bab38e74308c02be_gt.jpg b/media/arctic/6057bab38e74308c02be_gt.jpg new file mode 100644 index 0000000000000000000000000000000000000000..0f5f35161b9bd664f9af9ca32702a4a165cbfec6 Binary files /dev/null and b/media/arctic/6057bab38e74308c02be_gt.jpg differ diff --git a/media/arctic/6057bab38e74308c02be_rgb.jpg b/media/arctic/6057bab38e74308c02be_rgb.jpg new file mode 100644 index 0000000000000000000000000000000000000000..30e725e9c214780b6f4d2e0d342102cda25d2ef7 Binary files /dev/null and b/media/arctic/6057bab38e74308c02be_rgb.jpg differ diff --git a/media/arctic/a213dd95256ccccad7bc_gt.jpg b/media/arctic/a213dd95256ccccad7bc_gt.jpg new file mode 100644 index 0000000000000000000000000000000000000000..d2968fd7a297a20fdc4524619f10a1155bf8f5fd Binary files /dev/null and b/media/arctic/a213dd95256ccccad7bc_gt.jpg differ diff --git a/media/arctic/a213dd95256ccccad7bc_rgb.jpg b/media/arctic/a213dd95256ccccad7bc_rgb.jpg new file mode 100644 index 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var(--muted); border-top: 1px solid var(--lin
SOMA 手部数据图谱
@@ -237,17 +256,24 @@ footer { font-size: 0.8rem; color: var(--muted); border-top: 1px solid var(--lin "use strict"; var DATA = null, PREV = {}, BYID = {}, M = {}; var app = document.getElementById("app"); - var filt = { source: "", method: "", tier: "", status: "", q: "" }; + var filt = { source: "", method: "", tier: "", q: "", hasPrev: false }; + var ORDER = ["own", "p0", "p1", "p2", "used", "surveyed", "no"]; + var TIER_OF = { scan: "S1", marker: "S2", rgbd: "S3", mvkp: "S4", device: "-", synth: "SY", manual2d: "-", pseudo: "S5" }; function esc(s) { return String(s == null ? "" : s).replace(/[&<>"']/g, function (c) { return { "&": "&", "<": "<", ">": ">", '"': """, "'": "'" }[c]; }); } function by(list) { var o = {}; list.forEach(function (x) { o[x.id] = x; }); return o; } - function stChip(id) { var s = M.status[id]; return '' + esc(s.name) + ""; } - function tierChip(id, title) { var t = M.tier[id]; return '' + esc(id === "-" ? "—" : id) + ""; } - function dsLink(d, cls) { return '' + esc(d.name) + ""; } - function statusOrder(s) { return ["own", "p0", "p1", "p2", "used", "surveyed", "no"].indexOf(s); } - function sorted(list) { return list.slice().sort(function (a, b) { return statusOrder(a.status) - statusOrder(b.status) || a.name.localeCompare(b.name); }); } + function stChip(id) { return '' + esc(M.status[id].name) + ""; } + function tierChip(id) { var t = M.tier[id]; return '' + (id === "-" ? "—" : esc(id)) + ""; } + function nPrev(d) { return (PREV[d.id] || []).length; } + function dsLink(d, showN) { var n = nPrev(d); return '' + esc(d.name) + (showN && n ? ' ' + n + "" : "") + ""; } + function sorted(list) { return list.slice().sort(function (a, b) { return ORDER.indexOf(a.status) - ORDER.indexOf(b.status) || a.name.localeCompare(b.name); }); } + function partHead(title, note) { return '

' + esc(title) + "

" + (note ? "

" + note + "

" : "") + "
"; } + function thumb(d) { + var p = (PREV[d.id] || [])[0]; + return p ? '' : '暂无样例'; + } - /* ---------- 采集装置示意图 ---------- */ + /* ---------- 采集装置示意 ---------- */ function rigSvg(d) { var r = d.rig || {}, W = 320, H = 200, cx = 160, cy = 118, out = []; out.push(''); @@ -279,7 +305,6 @@ footer { font-size: 0.8rem; color: var(--muted); border-top: 1px solid var(--lin out.push(''); out.push('' + (r.pseudoEgo ? "固定于眼高（模拟第一视角）" : (r.fisheye ? "第一视角 · 宽视场" : "第一视角")) + (r.mono ? " · 黑白" : "") + ""); } - // 手 var hx2 = cx, hy2 = cy + 10; out.push(''); out.push(''); @@ -292,194 +317,191 @@ footer { font-size: 0.8rem; color: var(--muted); border-top: 1px solid var(--lin out.push(''); out.push('无 RGB 图像'); } - if (r.synth) out.push('渲染生成'); - if (r.wild && !r.ego) out.push('网络 / 日常图像'); - if (r.scanner) out.push('扫描仪多相机环'); + var foot = r.synth ? "渲染生成" : (r.wild && !r.ego) ? "网络 / 日常图像" : r.scanner ? "扫描仪多相机环" : ""; + if (foot) out.push('' + foot + ""); out.push(""); return out.join(""); } + function flow(steps) { return '
' + steps.map(function (s, i) { return (i ? '' : "") + '' + esc(s) + ""; }).join("") + "
"; } - function flow(steps) { - return '
' + steps.map(function (s, i) { return (i ? '' : "") + '' + esc(s) + ""; }).join("") + "
"; - } - - /* ---------- 视图：总览 ---------- */ + /* ---------- 总览 ---------- */ function vOverview() { var ds = DATA.datasets, cnt = {}; ds.forEach(function (d) { cnt[d.status] = (cnt[d.status] || 0) + 1; }); - var h = []; - h.push('

按数据来源和标注方法看手部数据

' + esc(DATA.meta.goal) + " 矩阵的行按 shape 真值的可信度从高到低排列，列是数据来源；点任一数据集看详情、采集示意和样例。

"); - h.push('
' + DATA.meta.statuses.map(function (s) { return "
" + stChip(s.id) + '' + (cnt[s.id] || 0) + "
"; }).join("") + '
共 ' + ds.length + " 个
"); - h.push('
'); + var withPrev = ds.filter(function (d) { return nPrev(d); }).length; + var h = ['
']; + h.push('

手部数据集，按来源和标注方法分类

' + esc(DATA.meta.goal) + "

"); + h.push('
' + ds.length + '个数据集
' + withPrev + '个有样例
' + ORDER.map(function (s) { return "
" + stChip(s) + "" + (cnt[s] || 0) + "
"; }).join("") + "
"); + + h.push('
' + partHead("分布矩阵", "行是标注方法，按 shape 真值可信度从高到低；列是数据来源。点名称看详情。")); + h.push('
标注方法 ＼ 数据来源
'); DATA.meta.sources.forEach(function (s) { h.push(""); }); h.push(""); - var tierOf = { scan: "S1", marker: "S2", rgbd: "S3", mvkp: "S4", device: "-", synth: "SY", manual2d: "-", pseudo: "S5" }; DATA.meta.methods.forEach(function (m) { - h.push('"); + h.push('"); DATA.meta.sources.forEach(function (s) { var list = sorted(ds.filter(function (d) { return d.method === m.id && d.source === s.id; })); - h.push(""); + h.push(""); }); h.push(""); }); - h.push("
标注方法" + esc(s.name) + "" + esc(s.desc) + "
' + esc(m.name) + "
" + tierChip(tierOf[m.id]) + "
' + esc(m.name) + "" + tierChip(TIER_OF[m.id]) + "" + (list.length ? '
' + list.map(function (d) { return dsLink(d); }).join("") + "
" : '·') + "		" + (list.length ? '
' + list.map(function (d) { return dsLink(d); }).join("") + "
" : '·') + "
"); - h.push('
标签颜色表示状态：' + DATA.meta.statuses.map(function (s) { return "" + stChip(s.id) + esc(s.desc) + ""; }).join("") + "
"); + h.push('
状态：' + ORDER.map(stChip).join("") + "
"); - h.push('

shape 真值分层

大部分数据集的 β 是从图像或关键点拟合的。监督 SOMA shape 时只用 S1 到 S3 和合成数据，S4 当弱监督，S5 和“—”不参与 shape。

'); - DATA.meta.tiers.forEach(function (t) { h.push("
" + tierChip(t.id) + "" + esc(t.name) + '' + esc(t.desc) + "
"); }); - h.push("
"); var p0 = ds.filter(function (d) { return d.status === "p0"; }); - h.push('

建议先接入

    '); - p0.forEach(function (d) { h.push('
  1. ' + esc(d.name) + "　" + esc(d.fills) + "
    2. "); }); - h.push('

完整理由和其余新增项见数据集目录，状态筛选选「P0 新增」到「P2 新增」。

'); - h.push("
"); + h.push('
' + partHead("建议先接入") + '
'); + p0.forEach(function (d) { h.push('' + stChip(d.status) + "" + esc(d.name) + '' + esc(d.fills) + ""); }); + h.push("
"); + h.push('
' + partHead("shape 真值分层", "只有 S1 到 S3 和合成数据用来监督 SOMA shape。") + '
'); + DATA.meta.tiers.forEach(function (t) { h.push("
" + tierChip(t.id) + "" + esc(t.name) + '' + esc(t.desc) + "
"); }); + h.push("
"); return h.join(""); } - /* ---------- 视图：目录 ---------- */ + /* ---------- 数据集目录 ---------- */ function opts(list, cur, all) { return '" + list.map(function (x) { return '"; }).join(""); } function vCatalog() { - var h = []; - h.push('

数据集目录

按数据来源、标注方法、shape 层级和状态筛选。每行是一个数据集，点开看完整信息。

'); - h.push(''); - h.push('

'); + var h = ['

数据集

按状态分组。缩略图是 GT 叠加样例，点开看完整信息。

']; + h.push('

'); + h.push('
'); return h.join(""); } function renderRows() { var q = filt.q.trim().toLowerCase(); - var list = sorted(DATA.datasets.filter(function (d) { + var list = DATA.datasets.filter(function (d) { if (filt.source && d.source !== filt.source) return false; if (filt.method && d.method !== filt.method) return false; if (filt.tier && d.tier !== filt.tier) return false; - if (filt.status && d.status !== filt.status) return false; + if (filt.hasPrev && !nPrev(d)) return false; if (q) { var hay = [d.name, d.camera, d.gt, d.fills, d.risk, d.scale, d.subjects, (d.tags || []).join(" ")].join(" ").toLowerCase(); if (hay.indexOf(q) < 0) return false; } return true; - })); - document.getElementById("cnt").textContent = "显示 " + list.length + " / " + DATA.datasets.length + " 个数据集"; - var h = ['
数据集数据来源标注方法真值shape规模状态
']; - if (!list.length) h.push('
没有符合条件的数据集。换一个筛选条件，或点「清除筛选」。
'); - list.forEach(function (d) { - h.push(''); - h.push('' + esc(d.name) + "" + esc(d.year) + ""); - h.push('数据来源' + esc(M.source[d.source].name) + ""); - h.push('标注方法' + esc(M.method[d.method].name) + ""); - h.push('真值' + esc(d.gt) + ""); - h.push('shape' + tierChip(d.tier) + ""); - h.push('规模' + esc([d.subjects, d.scale].filter(Boolean).join("；") || "—") + ""); - h.push('' + stChip(d.status) + ""); - h.push(""); }); - document.getElementById("rows").innerHTML = h.join(""); + document.getElementById("cnt").textContent = "显示 " + list.length + " / " + DATA.datasets.length + " 个"; + var h = []; + if (!list.length) h.push('

没有符合条件的数据集。换个条件，或点「清除」。

'); + ORDER.forEach(function (st) { + var g = list.filter(function (d) { return d.status === st; }).sort(function (a, b) { return a.name.localeCompare(b.name); }); + if (!g.length) return; + h.push('
' + stChip(st) + '' + esc(M.status[st].desc) + " · " + g.length + ' 个
'); + g.forEach(function (d) { + h.push('' + thumb(d)); + h.push('' + esc(d.name) + '' + esc(d.year) + ""); + h.push('' + esc(M.source[d.source].name) + '' + esc(M.method[d.method].name) + ""); + h.push('' + tierChip(d.tier) + " " + esc(d.gt) + ""); + h.push('' + esc([d.subjects, d.scale].filter(Boolean).join("；") || "规模未列") + ""); + }); + h.push("
"); + }); + document.getElementById("groups").innerHTML = h.join(""); } function bindCatalog() { - ["source", "method", "tier", "status"].forEach(function (k) { - document.getElementById("f-" + k).addEventListener("change", function (e) { filt[k] = e.target.value; renderRows(); }); - }); + ["source", "method", "tier"].forEach(function (k) { document.getElementById("f-" + k).addEventListener("change", function (e) { filt[k] = e.target.value; renderRows(); }); }); document.getElementById("f-q").addEventListener("input", function (e) { filt.q = e.target.value; renderRows(); }); + document.getElementById("f-prev").addEventListener("change", function (e) { filt.hasPrev = e.target.checked; renderRows(); }); document.getElementById("ff").addEventListener("submit", function (e) { e.preventDefault(); }); - document.getElementById("f-reset").addEventListener("click", function () { filt = { source: "", method: "", tier: "", status: "", q: "" }; render(); }); + document.getElementById("f-reset").addEventListener("click", function () { filt = { source: "", method: "", tier: "", q: "", hasPrev: false }; render(); }); renderRows(); } - /* ---------- 视图：标注方法 ---------- */ + /* ---------- 详情 ---------- */ + function vDetail(id) { + var d = BYID[id]; + if (!d) return '

找不到数据集「' + esc(id) + '」。回到数据集。

'; + var m = M.method[d.method], s = M.source[d.source], rt = M.route[d.route], t = M.tier[d.tier]; + var all = sorted(DATA.datasets), idx = all.indexOf(d), prev = all[idx - 1], next = all[idx + 1]; + var h = ['

数据集 / ' + esc(M.status[d.status].name) + "

"]; + + h.push('

' + esc(d.name) + '

' + stChip(d.status) + (d.year ? '' + esc(d.year) + "" : "") + (d.tags || []).filter(function (x) { return x !== "新增" && x !== "不建议"; }).map(function (x) { return '' + esc(x) + ""; }).join("") + '

' + esc(d.fills) + "

"); + + h.push('
'); + h.push('
数据来源' + esc(s.name) + "
"); + h.push('
标注方法' + esc(m.name) + "
"); + h.push('
真值' + esc(d.gt) + "
"); + h.push('
shape 层级' + tierChip(d.tier) + " " + esc(t.name) + "
"); + h.push('
转到 SOMA' + esc(rt.id + " " + rt.name) + "" + (rt.shape ? "" : '
不监督 shape') + "
"); + h.push("
"); + + var pv = PREV[d.id] || []; + h.push('
' + partHead("样例", pv.length ? "左：原始 RGB；右：GT 叠加。" : "")); + if (pv.length) { + h.push('
'); + pv.forEach(function (p, i) { + h.push('
' + esc(d.name +
RGB
' + esc(d.name +
' + esc(p.source || "GT 叠加") + "
" + (p.caption ? '

' + esc(p.caption) + "

" : "") + "
"); + }); + h.push("
"); + } else { + h.push('

还没有样例。添加方法见样例页。

' + ((d.team || []).length ? "

团队已有可视化：" + d.team.map(function (x) { return '' + esc(x.label.replace(/^团队(图册|视频)：/, "")) + ""; }).join("，") + "

" : "") + "
"); + } + h.push("
"); + + h.push('

相机与规模

'); + h.push("
相机
" + esc(d.camera || "未列") + "
"); + h.push("
受试者
" + esc(d.subjects || "未列") + "
"); + h.push("
规模
" + esc(d.scale || "未列") + "
"); + if (d.risk) h.push('
注意

' + esc(d.risk) + "

"); + h.push('

采集与标注流程

' + rigSvg(d) + flow(m.steps) + '

按相机配置绘制的示意，不是真实画面；琥珀色圆点表示手上贴了光学 marker。

'); + + var links = (d.links || []).concat((d.team || []).map(function (x) { return { label: x.label, url: x.url }; })); + if (links.length) h.push('
' + partHead("链接") + '
"); + h.push('
' + (prev ? '← ' + esc(prev.name) + "" : "") + "" + (next ? '' + esc(next.name) + " →" : "") + "
"); + return h.join(""); + } + + /* ---------- 标注方法 ---------- */ function vMethods() { - var h = ['

标注方法

同一种标注方法得到的真值，可信度和适用范围相近。下面按 shape 真值可信度从高到低排列，每种方法给出流程、可信度、常见问题和转到 SOMA 的路线。

']; - var tierOf = { scan: "S1", marker: "S2", rgbd: "S3", mvkp: "S4", device: "-", synth: "SY", manual2d: "-", pseudo: "S5" }; + var h = ['

标注方法

按 shape 真值可信度从高到低排列。每种方法列出流程、可信度、常见问题、转到 SOMA 的路线和用到它的数据集。

']; + h.push('"); DATA.meta.methods.forEach(function (m) { var list = sorted(DATA.datasets.filter(function (d) { return d.method === m.id; })); - h.push('

' + esc(m.name) + "

" + tierChip(tierOf[m.id]) + '' + list.length + " 个数据集
"); - h.push(flow(m.steps)); - h.push('
可信度
' + esc(m.trust) + "
常见问题
" + esc(m.issues) + '
SOMA 路线
' + esc(m.route) + "
"); - h.push('
' + list.map(function (d) { return dsLink(d); }).join("") + "
"); + h.push('

' + esc(m.name) + "

" + tierChip(TIER_OF[m.id]) + '' + list.length + " 个数据集
" + flow(m.steps)); + h.push('
可信度
' + esc(m.trust) + "
常见问题
" + esc(m.issues) + '
转到 SOMA
' + esc(m.route) + '
' + list.map(function (d) { return dsLink(d); }).join("") + "
"); }); h.push("
"); return h.join(""); } - /* ---------- 视图：SOMA 转换 ---------- */ + /* ---------- 转到 SOMA ---------- */ function vSoma() { - var h = ['

转到 SOMA hand

没有公开数据集原生使用 SOMA 标注。SOMA-X 的 SOMAHandLayer 官方支持三种手部后端：SOMA 原生（20 维手型 identity）、MANO 和 MHR，许可为 Apache-2.0。转到 SOMA 拓扑之后，要监督原生 shape 还需要再拟合一次。

']; - h.push('
'); - h.push('

原始真值

  • 扫描网格
  • MANO 参数或网格
  • SMPL-X / SMPL-H
  • MHR 参数
  • 3D 关键点
  • 2D 关键点
'); - h.push('

映射到 SOMA 拓扑

  • SOMAHandLayer 的 MANO 后端
  • SOMAHandLayer 的 MHR 后端
  • SMPL-X 先取手部 MANO 部分
  • 关键点先定关节对应
'); - h.push('

SOMA 原生重拟合

  • 拟合 20 维 identity 和 pose
  • 按数据集统计顶点残差
  • 残差超阈值的帧不监督 shape
'); - h.push('

训练监督

  • shape：S1 到 S3、合成数据
  • pose：所有 3D 来源
  • 2D 重投影：全部带 2D 的来源
  • 伪标签只做弱监督
'); - h.push("
"); - h.push('

转换路线

'); + var h = ['

转到 SOMA hand

没有公开数据集原生使用 SOMA 标注。SOMA-X 的 SOMAHandLayer 官方支持 SOMA 原生、MANO、MHR 三种手部后端（Apache-2.0）。映射到 SOMA 拓扑后，要监督原生 shape 还需要再拟合一次。

']; + h.push('
' + partHead("四步流程") + '
'); + h.push('
1 · 原始真值
  • 扫描网格
  • MANO 参数或网格
  • SMPL-X / SMPL-H
  • MHR 参数
  • 3D / 2D 关键点
'); + h.push('
2 · 映射到 SOMA 拓扑
  • SOMAHandLayer 的 MANO 后端
  • SOMAHandLayer 的 MHR 后端
  • SMPL-X 先取手部
  • 关键点先定关节对应
'); + h.push('
3 · SOMA 原生重拟合
  • 拟合 20 维 identity 和 pose
  • 按数据集统计顶点残差
  • 残差超阈值不监督 shape
'); + h.push('
4 · 训练监督
  • shape：S1 到 S3、合成
  • pose：所有 3D 来源
  • 2D 重投影：带 2D 的来源
  • 伪标签只做弱监督
'); + h.push("
"); + h.push('
' + partHead("转换路线", "每张卡片对应一种原始真值格式。") + '
'); DATA.meta.routes.forEach(function (r) { - var n = DATA.datasets.filter(function (d) { return d.route === r.id; }).length; - h.push('
' + r.id + "

" + esc(r.name) + '

来源：' + esc(r.from) + "

" + esc(r.how) + '

' + (r.shape ? '可监督 shape' : '不监督 shape') + ' · ' + n + " 个数据集

"); + var list = DATA.datasets.filter(function (d) { return d.route === r.id; }); + h.push('
' + r.id + "

" + esc(r.name) + '

' + esc(r.how) + '

' + (r.shape ? '可监督 shape' : '不监督 shape') + ' · ' + list.length + " 个数据集

"); }); - h.push("
"); - h.push('

需要注意

    '); - h.push("
  • MANO 的 21 点里指尖取的是网格顶点，与 SOMA 关节定义不同，关键点类数据集必须先定对应表。
    • "); - h.push("
  • EgoRenderer 已经做过一次原始网格到 MANO 的 778 顶点拟合，再转 SOMA 是第二次转换，残差要分开记录。
    • "); - h.push("
  • shape 和尺度在单目里是耦合的；有内参时，shape 误差会表现为绝对尺度误差，评测时建议单独报标准姿态下的顶点误差和骨长误差。
    • "); + h.push("
"); + h.push('
' + partHead("注意事项") + '
    '); + h.push("
  • MANO 21 点的指尖是网格顶点，与 SOMA 关节定义不同；关键点类数据集要先定对应表。
    • "); + h.push("
  • EgoRenderer 已经做过一次原始网格到 MANO 的拟合，再转 SOMA 是第二次，残差分开记录。
    • "); + h.push("
  • 单目下 shape 和尺度耦合；评测单独报标准姿态下的顶点误差和骨长误差。
    • "); h.push("
  • SAM 3D Body 输出 MHR，可以走 R5 给野外图像生成伪标签；它的训练数据是否公开未查到。
    • "); - h.push('

参考

'); - h.push("
"); + h.push('
' + partHead("参考") + '
'); return h.join(""); } - /* ---------- 视图：样例素材 ---------- */ + /* ---------- 样例 ---------- */ function vSamples() { - var h = ['

样例素材

每个数据集的详情页会显示「RGB / GT 叠加」样例对。样例登记在 data/previews.json，图片放在 media/<数据集 id>/。用脚本添加，不需要改页面代码。

']; - h.push('

添加一对样例

python tools/add_preview.py hograspnet \\\n  --rgb  /data/hograspnet/s01_0042_rgb.png \\\n  --gt   /data/hograspnet/s01_0042_gt.png \\\n  --caption "受试者 01，侧捏，第一视角相机" \\\n  --source "官方 MANO，经 R1 转为 SOMA 后渲染"

脚本会把图片转成最长边 960 像素的 JPEG 复制到 media 目录，并追加到 previews.json。同名样例会被覆盖。

'); - h.push('

previews.json 格式

{\n  "hograspnet": [\n    {\n      "rgb": "media/hograspnet/s01_0042_rgb.jpg",\n      "gt": "media/hograspnet/s01_0042_gt.jpg",\n      "caption": "受试者 01，侧捏",\n      "source": "官方 MANO → SOMA"\n    }\n  ]\n}
'); - h.push('

当前样例覆盖

'); - sorted(DATA.datasets).forEach(function (d) { - var n = (PREV[d.id] || []).length; - h.push('"); - }); - h.push("
数据集状态本站样例团队已有可视化
' + esc(d.name) + "" + stChip(d.status) + '' + (n || "—") + "" + ((d.team || []).map(function (t) { return '' + esc(t.label.replace("团队图册：", "").replace("团队视频：", "视频：")) + ""; }).join("
") || '无') + "
"); - return h.join(""); - } - - /* ---------- 视图：详情 ---------- */ - function vDetail(id) { - var d = BYID[id]; - if (!d) return '

找不到数据集「' + esc(id) + '」。回到数据集目录。

'; - var m = M.method[d.method], s = M.source[d.source], rt = M.route[d.route], t = M.tier[d.tier]; - var all = sorted(DATA.datasets), idx = all.indexOf(d), prev = all[idx - 1], next = all[idx + 1]; - var h = ['

数据集目录 / ' + esc(s.name) + " / " + esc(m.name) + "

"]; - h.push('

' + esc(d.name) + '

' + stChip(d.status) + (d.tags || []).map(function (x) { return '' + esc(x) + ""; }).join("") + '' + esc(d.year) + "
"); - h.push('

基本信息

'); - h.push("
数据来源
" + esc(s.name) + '
' + esc(s.desc) + "
"); - h.push("
相机
" + esc(d.camera || "—") + "
"); - h.push('
标注方法
' + esc(m.name) + "
"); - h.push("
真值格式
" + esc(d.gt) + "
"); - h.push("
shape 层级
" + tierChip(d.tier) + " " + esc(t.name) + '，' + esc(t.desc) + "
"); - h.push('
SOMA 路线
' + esc(rt.id + " " + rt.name) + "" + (rt.shape ? "" : '，不监督 shape') + "
"); - h.push("
受试者
" + esc(d.subjects || "—") + "
"); - h.push("
规模
" + esc(d.scale || "—") + "
"); - h.push('
补什么

' + esc(d.fills) + "

"); - if (d.risk) h.push('
注意

' + esc(d.risk) + "

"); - h.push("
"); - h.push('

采集与标注示意

' + rigSvg(d) + flow(m.steps) + '

示意图按数据集的相机配置绘制，不是真实画面。琥珀色圆点表示手上贴有光学 marker。

'); - var pv = PREV[d.id] || []; - h.push('

样例：RGB 与 GT 叠加

'); - if (pv.length) { - h.push('
'); - pv.forEach(function (p, i) { - h.push('
' + esc(d.name +
RGB
' + esc(d.name +
GT 叠加' + (p.source ? "：" + esc(p.source) : "") + "
"); - if (p.caption) h.push('

' + esc(p.caption) + "

"); - }); - h.push("
"); - } else { - h.push('

本站还没有这个数据集的样例。用 tools/add_preview.py ' + esc(d.id) + " 添加，步骤见样例素材。

"); - if ((d.team || []).length) h.push("

团队已有的可视化：" + d.team.map(function (x) { return '' + esc(x.label) + ""; }).join("，") + "

"); - h.push("
"); - } - h.push("
"); - if ((d.links || []).length) h.push('

链接

"); - h.push('
' + (prev ? '← ' + esc(prev.name) + "" : "") + "" + (next ? '' + esc(next.name) + " →" : "") + "
"); - h.push("
"); + var ds = sorted(DATA.datasets), have = ds.filter(nPrev), none = ds.filter(function (d) { return !nPrev(d); }); + var pairs = have.reduce(function (a, d) { return a + nPrev(d); }, 0); + var h = ['

样例

每对样例是同一帧的原始 RGB 和 GT 叠加。图片放在独立的 HF Dataset 仓库或本站 media 目录，登记在 data/previews.json。

']; + h.push('
' + partHead("覆盖情况", have.length + " 个数据集共 " + pairs + " 对；" + none.length + " 个还没有。") ); + h.push('
已有样例（数字为对数）
' + have.map(function (d) { return dsLink(d, true); }).join("") + "
"); + h.push('
暂无样例
' + none.map(function (d) { return dsLink(d); }).join("") + "
"); + h.push('
' + partHead("怎么添加", "三种方式，按场景选一种。") + '
'); + h.push('

在数据所在的服务器上批量同步（推荐）

在存放数据集的机器上运行。图片传到独立的 HF Dataset 仓库并固定版本，Space 只登记 URL，网页不依赖 SSH。可以直接给已渲染好的叠加图，也可以给原图加网格（MANO 或 SOMA）由脚本渲染。

export HF_TOKEN=hf_xxx\npython tools/server_sync.py manifest.jsonl \\\n  --media-repo DarthSoluke/EgoNetDatasetResearch-media \\\n  --space DarthSoluke/EgoNetDatasetResearch
{"dataset": "hograspnet",\n "rgb": "/SSD/hograspnet/s01/0042.png",\n "mesh": "/SSD/hograspnet/s01/0042_soma.npz",\n "caption": "受试者 01，侧捏",\n "source": "官方 MANO → R1 → SOMA"}
'); + h.push('

从团队图册导入

复用 nine-source Space 里已经做好的叠加图，每个数据集取若干对。

python tools/import_team_previews.py --per 4
'); + h.push('

手动加一对

已有一张 RGB 和一张叠加图时用。

python tools/add_preview.py hograspnet \\\n  --rgb a_rgb.png --gt a_gt.png \\\n  --caption "说明" --source "GT 来源"
'); + h.push("
"); return h.join(""); } @@ -493,7 +515,7 @@ footer { font-size: 0.8rem; color: var(--muted); border-top: 1px solid var(--lin else if (hash === "soma") html = vSoma(); else if (hash === "samples") html = vSamples(); else { html = vOverview(); view = "overview"; } - app.innerHTML = html + '
数据整理于 ' + esc(DATA.meta.updated) + "。数值来自论文、项目页和团队已有图册；标为「待核」的字段尚未确认。修改数据请编辑 tools/make_data.py 后重新生成。
"; + app.innerHTML = html + '
整理于 ' + esc(DATA.meta.updated) + "。数值来自论文、项目页和团队图册；标「待核」的字段尚未确认。
"; document.querySelectorAll("nav.tabs a").forEach(function (a) { if (a.getAttribute("data-v") === view) a.setAttribute("aria-current", "page"); else a.removeAttribute("aria-current"); }); if (hash === "catalog") bindCatalog(); if (pendingJump) { var el = document.getElementById(pendingJump); pendingJump = null; if (el) { el.scrollIntoView(); return; } } @@ -516,7 +538,7 @@ footer { font-size: 0.8rem; color: var(--muted); border-top: 1px solid var(--lin fetch("data/datasets.json").then(function (r) { if (!r.ok) throw new Error(r.status); return r.json(); }), fetch("data/previews.json").then(function (r) { return r.ok ? r.json() : {}; }).catch(function () { return {}; }) ]).then(function (x) { boot(x[0], x[1]); }).catch(function () { - app.innerHTML = '

没能读取 data/datasets.json。直接双击打开 HTML 文件时浏览器会拦截读取，请用 python -m http.server 在本目录启动后访问，或部署到 Hugging Face Space。

'; + app.innerHTML = '

没能读取 data/datasets.json。直接双击打开 HTML 时浏览器会拦截读取，请在本目录运行 python -m http.server 后访问，或部署到 Hugging Face Space。

'; }); })(); diff --git a/tools/import_team_previews.py b/tools/import_team_previews.py new file mode 100644 index 0000000000000000000000000000000000000000..3ea9bf1e419bbce2fd439f19154b9d1cbaa5da89 --- /dev/null +++ b/tools/import_team_previews.py @@ -0,0 +1,120 @@ +#!/usr/bin/env python3 +"""从团队 Space（yangzijing/nine-source-hand-data-browser）导入已有的 RGB / GT 叠加样例。 + + python tools/import_team_previews.py [--per 4] + +读取两份团队图册的数据（WiLoR 14 源图册、第一人称具身图册），按数据集取样例对， +缩放成 JPEG 存到 media//，写入 data/previews.json。只覆盖本脚本导入的条目， +用 add_preview.py 手动加的样例会保留。 +""" +import argparse +import io +import json +import re +import sys +import urllib.request +from pathlib import Path + +ROOT = Path(__file__).resolve().parent.parent +BASE = "https://huggingface.co/spaces/yangzijing/nine-source-hand-data-browser/resolve/main" +WILOR_DIR = "hand_dataset_browser_20261013" +EGO_DIR = "egocentric_embodied_datasets_20261020" +TAG = "团队图册" + +WILOR_MAP = {"arctic": "arctic", "h2o": "h2o", "hot3d": "hot3d", "mtc": "mtc", "dexycb": "dexycb", + "Re:InterHand": "reinterhand", "InterHand2.6M": "interhand", "HO3D": "ho3d", "H2O3D": "h2o3d", + "FreiHAND": "freihand", "RHD": "rhd", "COCO WholeBody": "cocowb", "Halpe": "halpe", + "MPII NZSL": "mpiinzsl", "egorenderer_v005": "egorenderer"} +EGO_MAP = {"xperience": "xperience", "gen-humanego": "genhumanego", "egoverse": "egoverse", "egoemg": "egoemg", + "egodex": "egodex", "openaoe": "openaoe", "taco": "taco", "hocap": "hocap", + "egopressure": "egopressure", "show3d": "show3d"} +HAND = {"left": "左手", "right": "右手"} + + +def get(url): + with urllib.request.urlopen(url, timeout=60) as r: + return r.read() + + +def save_jpg(raw, dst, max_side): + from PIL import Image + im = Image.open(io.BytesIO(raw)).convert("RGB") + s = max_side / max(im.size) + if s < 1: + im = im.resize((round(im.width * s), round(im.height * s)), Image.LANCZOS) + dst.parent.mkdir(parents=True, exist_ok=True) + im.save(dst, "JPEG", quality=86, optimize=True) + + +def pick(lst, n): + if len(lst) <= n: + return lst + return [lst[round(i * (len(lst) - 1) / (n - 1))] for i in range(n)] + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--per", type=int, default=4, help="每个数据集最多导入几对") + ap.add_argument("--max-side", type=int, default=960) + a = ap.parse_args() + + ids = {d["id"] for d in json.loads((ROOT / "data/datasets.json").read_text(encoding="utf-8"))["datasets"]} + jobs = {} # id -> [(rgb_url, gt_url, name, caption, source)] + + html = get(f"{BASE}/{WILOR_DIR}/index.html").decode("utf-8") + js = html[html.index("const DATA"):] + g, _ = json.JSONDecoder().raw_decode(js[js.index("{"):]) + assets = g["assets"] + for d in g["datasets"]: + key = WILOR_MAP.get(d["id"]) or WILOR_MAP.get(d["name"]) + if not key or key not in ids: + continue + for s in pick(d.get("samples", []), a.per): + ov = next(iter((s.get("overlays") or {}).items()), None) + if not ov or s["rgb"] not in assets or ov[1] not in assets: + continue + cap = " · ".join(x for x in [s.get("recording"), f"帧 {s['frame']}" if s.get("frame") is not None else "", + HAND.get(s.get("hand"), ""), s.get("split")] if x) + src = f"{ov[0]}（{TAG} {g.get('date', '')}）" + jobs.setdefault(key, []).append((f"{BASE}/{WILOR_DIR}/{assets[s['rgb']]}", f"{BASE}/{WILOR_DIR}/{assets[ov[1]]}", + re.sub(r"\W", "_", s["id"]), cap, src)) + + e = json.loads(get(f"{BASE}/{EGO_DIR}/sample_evidence.json")) + for k, lst in e["samples"].items(): + key = EGO_MAP.get(k) + if not key or key not in ids: + continue + for i, s in enumerate(pick(lst, a.per)): + if not s.get("rgb") or not s.get("overlay"): + continue + cap = " · ".join(x for x in [s.get("frame"), s.get("camera"), s.get("license")] if x) + src = f"{s.get('overlay_label') or s.get('label_type') or 'GT 叠加'}（{TAG} {e.get('date', '')}）" + jobs.setdefault(key, []).append((f"{BASE}/{EGO_DIR}/{s['rgb']}", f"{BASE}/{EGO_DIR}/{s['overlay']}", + f"team_{k}_{i}", cap, src)) + + pf = ROOT / "data/previews.json" + prev = json.loads(pf.read_text(encoding="utf-8")) if pf.exists() else {} + total = 0 + for key, items in sorted(jobs.items()): + kept = [p for p in prev.get(key, []) if TAG not in p.get("source", "")] + new = [] + for rgb_u, gt_u, name, cap, src in items: + try: + r_rgb, r_gt = f"media/{key}/{name}_rgb.jpg", f"media/{key}/{name}_gt.jpg" + save_jpg(get(rgb_u), ROOT / r_rgb, a.max_side) + save_jpg(get(gt_u), ROOT / r_gt, a.max_side) + new.append({"rgb": r_rgb, "gt": r_gt, "caption": cap, "source": src}) + except Exception as ex: # noqa: BLE001 + print(f" 跳过 {key}/{name}：{ex}", file=sys.stderr) + if new + kept: + prev[key] = new + kept + else: + prev.pop(key, None) + total += len(new) + print(f"{key}: {len(new)} 对") + pf.write_text(json.dumps(prev, ensure_ascii=False, indent=1) + "\n", encoding="utf-8") + print(f"共导入 {total} 对，覆盖 {len(jobs)} 个数据集") + + +if __name__ == "__main__": + main() diff --git a/tools/make_data.py b/tools/make_data.py index 9d16630378eb1f0f3767ab52bd8c07ae695e724d..39749ae99aa3c54e0e7e56c21dea8352f0273258 100644 --- a/tools/make_data.py +++ b/tools/make_data.py @@ -267,24 +267,24 @@ add(id="egopressure", name="EgoPressure", year="CVPR 2025", status="surveyed", t source="egoexo", method="rgbd", gt="MANO", tier="S3", route="R1", camera="头戴相机 + 多台外视角 RGB-D", rig={"ego": True, "exo": 7, "depth": True}, fills="手与表面接触的压力，带第一视角。", + risk="HaMeR 初始化后用多视角 RGB-D、分割、压力和时序约束拟合 MANO，手上没有 marker。", links=[arxiv("2409.02224")], team=[T_EGO]) add(id="egoemg", name="EgoEMG", year="2026", status="surveyed", tags=["第一人称图册"], - source="ego", method="marker", gt="手部 3D（光学动捕）", tier="S2", route="R1", - camera="第一视角 + 26 相机光学动捕", rig={"ego": True}, + source="ego", method="marker", gt="MANO（光捕 marker 经 markers2mano 拟合）", tier="S2", route="R1", + camera="头戴 GoPro 60 FPS + 26 相机光学动捕", rig={"ego": True}, fills="第一视角与肌电同步，光学动捕真值。", - risk="手部模型格式待核。", links=[arxiv("2605.05712")], team=[T_EGO]) add(id="hocap", name="HO-Cap", year="2024", status="surveyed", tags=["第一人称图册"], source="egoexo", method="rgbd", gt="MANO", tier="S3", route="R1", - camera="多台 RGB-D + 头显第一视角", rig={"ego": True, "exo": 8, "depth": True}, + camera="多台 RGB-D + HoloLens 第一视角 RGB", rig={"ego": True, "exo": 8, "depth": True}, fills="手物交互，含第一视角。", - risk="标注细节待核。", + risk="多视角 RGB-D 与关键点约束拟合 MANO，不使用光捕。", team=[T_EGO]) add(id="taco", name="TACO", year="CVPR 2024", status="surveyed", tags=["第一人称图册"], source="egoexo", method="mvkp", gt="MANO", tier="S4", route="R1", camera="多台外视角 + 1 个第一视角", rig={"ego": True, "exo": 12}, fills="双手使用工具的交互。", - risk="标注方式待核，暂归入关键点三角化。", + risk="多视角 MMPose 三角化后做 MANO 接触与时序拟合，手上没有光捕。", team=[T_EGO]) add(id="aceda0", name="ACE-Data-0", year="2026", status="surveyed", tags=["第一人称图册"], source="egoexo", method="marker", gt="手部动捕（格式待核）", tier="S2", route="R1", @@ -412,10 +412,10 @@ add(id="assemblyhands", name="AssemblyHands", year="CVPR 2023", status="no", tag risk="第一视角不是真实 RGB，与排除 SHOW3D 的理由相同。", links=[arxiv("2304.12301")]) add(id="show3d", name="SHOW3D", year="2026", status="no", tags=["不建议", "第一人称图册"], - source="norgb", method="pseudo", gt="自动标注为主", tier="S5", route="R7", + source="norgb", method="mvkp", gt="UmeTrack（另提供 MANO / MHR 转换）", tier="S4", route="R1", camera="黑白鱼眼", rig={"ego": True, "fisheye": True, "mono": True}, fills="团队已排除。", - risk="主标注自动生成，人工核验子集不具代表性；黑白鱼眼不满足真实 RGB 条件。", + risk="无标记多视角自动拟合，人工核验子集不具代表性；黑白鱼眼不满足真实 RGB 条件。", team=[T_EGO]) add(id="hiphi", name="HiPHI", year="2026", status="no", tags=["不建议"], source="norgb", method="marker", gt="BVH 骨架", tier="-", route="R3", diff --git a/tools/render_overlay.py b/tools/render_overlay.py new file mode 100644 index 0000000000000000000000000000000000000000..2b79b64347f25f069f0ad0ba7dcb436f61efe747 --- /dev/null +++ b/tools/render_overlay.py @@ -0,0 +1,90 @@ +#!/usr/bin/env python3 +"""把手部网格（MANO、SOMA 或任意三角网格）投影到 RGB 上，生成半透明叠加图。 + +只依赖 numpy 和 Pillow。可单独运行，也被 server_sync.py 调用： + + python tools/render_overlay.py --image frame.png --mesh hand.npz --out frame_gt.png + +mesh.npz 需要： + verts (N, 3) 相机坐标系下的顶点，单位米，z 朝前 + faces (F, 3) 三角面索引 + K (3, 3) 内参（也可用 --K 传入 JSON） +可选： + verts 可以是 (H, N, 3)，表示多只手；faces 共用 + joints (J, 3) 或 (H, J, 3)，会画成小圆点 +""" +import argparse +import json + +import numpy as np +from PIL import Image, ImageDraw + +HAND_COLORS = [(70, 140, 230), (235, 140, 50)] # 右手蓝、左手橙（按传入顺序） + + +def project(p, K): + z = np.clip(p[:, 2], 1e-6, None) + return np.stack([K[0, 0] * p[:, 0] / z + K[0, 2], K[1, 1] * p[:, 1] / z + K[1, 2]], 1) + + +def overlay(img, verts, faces, K, joints=None, alpha=0.55, colors=HAND_COLORS): + """img: PIL.Image；verts: (N,3) 或 (H,N,3)；返回新的 PIL.Image。""" + base = img.convert("RGBA") + layer = Image.new("RGBA", base.size, (0, 0, 0, 0)) + draw = ImageDraw.Draw(layer) + V = np.asarray(verts, float) + if V.ndim == 2: + V = V[None] + F = np.asarray(faces, int) + K = np.asarray(K, float) + light = np.array([0.3, -0.4, -0.85]) + light /= np.linalg.norm(light) + + tris = [] # (depth, polygon, rgba) + for h, v in enumerate(V): + col = np.array(colors[h % len(colors)], float) + uv = project(v, K) + a, b, c = v[F[:, 0]], v[F[:, 1]], v[F[:, 2]] + n = np.cross(b - a, c - a) + n /= np.linalg.norm(n, axis=1, keepdims=True) + 1e-12 + shade = 0.45 + 0.55 * np.abs(n @ light) + depth = (a[:, 2] + b[:, 2] + c[:, 2]) / 3 + ok = (a[:, 2] > 0) & (b[:, 2] > 0) & (c[:, 2] > 0) + for i in np.nonzero(ok)[0]: + poly = [tuple(uv[j]) for j in F[i]] + rgb = tuple(int(x) for x in np.clip(col * shade[i], 0, 255)) + tris.append((depth[i], poly, rgb + (int(255 * alpha),))) + for _, poly, rgba in sorted(tris, key=lambda t: -t[0]): # 远的先画 + draw.polygon(poly, fill=rgba) + out = Image.alpha_composite(base, layer) + + if joints is not None: + J = np.asarray(joints, float) + if J.ndim == 2: + J = J[None] + d = ImageDraw.Draw(out) + r = max(2, round(min(out.size) / 220)) + for j in J: + for x, y in project(j, K): + d.ellipse([x - r, y - r, x + r, y + r], fill=(255, 255, 255, 255), outline=(20, 20, 20, 255)) + return out.convert("RGB") + + +def main(): + ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--image", required=True) + ap.add_argument("--mesh", required=True, help="npz：verts, faces, 可选 K / joints") + ap.add_argument("--K", help="内参 JSON，例如 [[fx,0,cx],[0,fy,cy],[0,0,1]]") + ap.add_argument("--alpha", type=float, default=0.55) + ap.add_argument("--out", required=True) + a = ap.parse_args() + m = np.load(a.mesh) + K = np.array(json.loads(a.K)) if a.K else m["K"] + img = overlay(Image.open(a.image), m["verts"], m["faces"], K, + joints=m["joints"] if "joints" in m.files else None, alpha=a.alpha) + img.save(a.out) + print("写出", a.out) + + +if __name__ == "__main__": + main() diff --git a/tools/server_sync.py b/tools/server_sync.py new file mode 100644 index 0000000000000000000000000000000000000000..c197b7ab291371cc760e19df15339bc729628b12 --- /dev/null +++ b/tools/server_sync.py @@ -0,0 +1,136 @@ +#!/usr/bin/env python3 +"""在存放数据集的服务器上运行：批量生成样例并同步到 Hugging Face。 + +做法与团队 nine-source Space 一致：图片放进一个独立的 HF Dataset 仓库， +Space 只登记固定版本的 URL，网页不依赖 SSH、本机服务或临时隧道。 + + export HF_TOKEN=hf_xxx # 或先 hf auth login + python tools/server_sync.py manifest.jsonl \ + --media-repo DarthSoluke/EgoNetDatasetResearch-media \ + --space DarthSoluke/EgoNetDatasetResearch + +manifest.jsonl 每行一个样例，两种写法任选： + + 已经渲染好的叠加图 + {"dataset": "hograspnet", "rgb": "/SSD/.../0042.png", "gt": "/SSD/.../0042_overlay.png", + "caption": "受试者 01，侧捏", "source": "官方 MANO"} + + 只有原图和网格，由本脚本渲染（网格可以是 MANO，也可以是转换后的 SOMA） + {"dataset": "hograspnet", "rgb": "/SSD/.../0042.png", "mesh": "/SSD/.../0042_soma.npz", + "caption": "受试者 01，侧捏", "source": "官方 MANO → R1 → SOMA"} + mesh.npz 的格式见 render_overlay.py（verts、faces、K，可选 joints） + +可选字段：name（样例名，默认取 rgb 文件名）。 + +参数： + --replace 先清掉这些数据集里以前由本脚本同步的样例，再写入新的 + --local-only 只写到本地目录 --out（默认 ./sync_out），不上传，用于先检查 +""" +import argparse +import json +import os +import re +import shutil +import sys +import tempfile +from pathlib import Path + +from PIL import Image + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from render_overlay import overlay # noqa: E402 + +ORIGIN = "server_sync" + + +def to_jpg(img, dst, max_side): + img = img.convert("RGB") + s = max_side / max(img.size) + if s < 1: + img = img.resize((round(img.width * s), round(img.height * s)), Image.LANCZOS) + dst.parent.mkdir(parents=True, exist_ok=True) + img.save(dst, "JPEG", quality=86, optimize=True) + + +def build(manifest, stage, max_side): + import numpy as np + rows = [json.loads(l) for l in Path(manifest).read_text(encoding="utf-8").splitlines() if l.strip()] + items = {} + for i, r in enumerate(rows, 1): + ds = r["dataset"] + name = re.sub(r"[^\w\-]", "_", r.get("name") or Path(r["rgb"]).stem.removesuffix("_rgb")) + rgb = Image.open(r["rgb"]) + if r.get("gt"): + gt = Image.open(r["gt"]) + elif r.get("mesh"): + m = np.load(r["mesh"]) + K = np.array(r["K"]) if r.get("K") else m["K"] + gt = overlay(rgb, m["verts"], m["faces"], K, joints=m["joints"] if "joints" in m.files else None) + else: + sys.exit(f"第 {i} 行既没有 gt 也没有 mesh") + rel_rgb, rel_gt = f"media/{ds}/{name}_rgb.jpg", f"media/{ds}/{name}_gt.jpg" + to_jpg(rgb, stage / rel_rgb, max_side) + to_jpg(gt, stage / rel_gt, max_side) + items.setdefault(ds, []).append({"rgb": rel_rgb, "gt": rel_gt, "caption": r.get("caption", ""), + "source": r.get("source", ""), "origin": ORIGIN}) + print(f"[{i}/{len(rows)}] {ds}/{name}") + return items + + +def merge(prev, items, prefix, replace): + for ds, new in items.items(): + old = prev.get(ds, []) + if replace: + old = [p for p in old if p.get("origin") != ORIGIN] + new_paths = {prefix + n["rgb"] for n in new} + old = [p for p in old if p.get("rgb") not in new_paths] + prev[ds] = [dict(n, rgb=prefix + n["rgb"], gt=prefix + n["gt"]) for n in new] + old + return prev + + +def main(): + ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("manifest") + ap.add_argument("--media-repo", default="DarthSoluke/EgoNetDatasetResearch-media") + ap.add_argument("--space", default="DarthSoluke/EgoNetDatasetResearch") + ap.add_argument("--max-side", type=int, default=960) + ap.add_argument("--replace", action="store_true") + ap.add_argument("--local-only", action="store_true") + ap.add_argument("--out", default="sync_out") + a = ap.parse_args() + + if a.local_only: + out = Path(a.out) + items = build(a.manifest, out, a.max_side) + pf = out / "data/previews.json" + prev = json.loads(pf.read_text(encoding="utf-8")) if pf.exists() else {} + pf.parent.mkdir(parents=True, exist_ok=True) + pf.write_text(json.dumps(merge(prev, items, "", a.replace), ensure_ascii=False, indent=1) + "\n", encoding="utf-8") + print(f"已写到 {out}/，检查无误后去掉 --local-only 重新运行") + return + + from huggingface_hub import HfApi, hf_hub_download + api = HfApi(token=os.environ.get("HF_TOKEN")) + stage = Path(tempfile.mkdtemp(prefix="soma_sync_")) + try: + items = build(a.manifest, stage, a.max_side) + api.create_repo(a.media_repo, repo_type="dataset", exist_ok=True) + info = api.upload_folder(repo_id=a.media_repo, repo_type="dataset", folder_path=str(stage), + commit_message=f"同步 {sum(map(len, items.values()))} 对样例：{', '.join(sorted(items))}") + rev = info.oid + prefix = f"https://huggingface.co/datasets/{a.media_repo}/resolve/{rev}/" + + cur = hf_hub_download(a.space, "data/previews.json", repo_type="space", token=api.token) + prev = json.loads(Path(cur).read_text(encoding="utf-8")) + merged = merge(prev, items, prefix, a.replace) + tmp = stage / "previews.json" + tmp.write_text(json.dumps(merged, ensure_ascii=False, indent=1) + "\n", encoding="utf-8") + api.upload_file(path_or_fileobj=str(tmp), path_in_repo="data/previews.json", repo_id=a.space, repo_type="space", + commit_message=f"登记样例（媒体版本 {rev[:8]}）") + print(f"完成：媒体仓库版本 {rev}，已更新 {a.space} 的 data/previews.json") + finally: + shutil.rmtree(stage, ignore_errors=True) + + +if __name__ == "__main__": + main()