diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..26179d0e3921e1fb610f2cff42d0506d672bb630 --- /dev/null +++ b/README.md @@ -0,0 +1,394 @@ +# Geo4D 최종 Evaluation Set 설명 및 분류 + +이 문서는 `/workspace/dataset/geo4d_evals`에 포함된 최종 evaluation set 5종을 설명하고, 데이터의 획득 방식과 평가 관계를 기준으로 분류한다. 수치와 경로는 2026-08-31 KST 기준 각 데이터셋의 `manifest.json`을 기준으로 집계했다. 공통 저장 규약의 세부 정의는 [`../dataformat.md`](../dataformat.md)를 따른다. + +## 1. 전체 구성 + +최종 evaluation set은 다음 5개 데이터셋으로 구성된다. + +```text +Geo4D evaluation sets +├── Monocular in-the-wild video +│ ├── 관측 RGB + 합성 novel camera-only control: DynPose, SpatialVID +│ └── 관측 RGB + 제공된 novel camera-only control: Vista4D +├── Multi-view video +│ └── 동일 원본 time ID의 실제 source-target RGB: DyCheck +└── Multi-view, novel-time video + └── camera-time grid에서 시점과 시간을 함께 재조합: CamXTime +``` + +여기서 분류 기준은 다음과 같다. + +- **Monocular in-the-wild video**: 하나의 일반 영상이 주된 RGB 관측이며, 같은 장면의 동기화된 별도 카메라 target RGB가 없다. +- **Multi-view video**: 같은 동적 장면을 서로 다른 실제 카메라에서 관측한 RGB가 있고, source와 target을 원본 시간 기준으로 대응시킬 수 있다. +- **Multi-view, novel-time video**: 여러 카메라와 여러 시간의 RGB grid가 있어, camera 경로뿐 아니라 시간 진행 방식도 source와 다르게 구성할 수 있다. + +### 1.1 한눈에 보는 비교 + +| 데이터셋 | 주 분류 | 고유 source | 평가 case | paired target | camera-only control | 프레임 길이 | FPS | +|---|---|---:|---:|---:|---:|---|---| +| DynPose | Monocular in-the-wild + synthetic novel-camera control | 377 | 4,524 | 0 | 4,524 | 83–256 | scene별 가변 | +| SpatialVID | Monocular in-the-wild + synthetic novel-camera control | 200 | 2,400 | 0 | 2,400 | 81 고정 | 출력 FPS 미정의 | +| Vista4D | Monocular in-the-wild + novel-camera control | 51 | 110 | 0 | 110 | 49 고정 | 24/25/30 | +| DyCheck | Multi-view | 20 | 20 | 13 | 0 | 180–424 | 30/60 | +| CamXTime | Multi-view, novel-time | 31 | 217 | 217 | 0 | 81 고정 | 30 | + +합계는 고유 source 디렉터리 679개, 평가 case 7,271개, paired target 230개다. 물리적으로 저장된 RGB는 source 86,842프레임과 target 21,505프레임으로 총 108,347프레임이다. DynPose, SpatialVID, Vista4D에는 RGB target 대신 각각 718,188프레임, 194,400프레임, 5,390프레임, 총 917,978프레임의 camera-control pose가 별도로 있다. + +> **중요:** `평가 case`와 `고유 source`는 같은 개념이 아니다. DynPose와 SpatialVID는 source마다 12개 합성 trajectory를 연결해 각각 4,524개와 2,400개 case를 만들고, CamXTime은 source 31개를 7가지 target temporal pattern에 사용해 217개 case를 만들며, Vista4D는 source 51개에 110개 camera trajectory를 연결한다. + +## 2. 카테고리별 분류 + +### 2.1 Monocular in-the-wild video + +이 그룹의 공통점은 실제 환경에서 얻은 일반 단안 영상이 source이고, 동일 장면을 다른 실제 카메라로 촬영한 paired target RGB가 없다는 점이다. 다만 평가 신호의 형태에 따라 다시 두 군집으로 나뉜다. + +#### 관측 RGB와 novel camera-only control 제공 + +- **DynPose**: YouTube RGB와 ViPE 관측 pose에 NeoVerse demo의 12개 표준 relative trajectory를 합성한다. +- **SpatialVID**: 관측 source pose에 같은 12개 표준 relative trajectory를 합성한다. +- **Vista4D**: Pexels/DAVIS source 영상과 별도의 목표 camera trajectory를 제공한다. + +세 데이터셋 모두 입력은 monocular in-the-wild이지만 평가 조건에는 novel camera path가 포함된다. DynPose/SpatialVID control은 원본 제공 신호가 아니라 명시적으로 정의한 synthetic benchmark control이고, Vista4D control은 원 평가 데이터에서 가져왔다. 실제 target RGB는 없으므로 full-reference novel-view metric보다는 camera-conditioned generation이나 trajectory-following 평가에 적합하다. DynPose/SpatialVID의 source pose 자체는 계속 관측 RGB에 대응하는 pose이며 목표 path로 오해하면 안 된다. + +### 2.2 Multi-view video + +- **DyCheck**는 동적 장면을 camera 0 source와 camera 1/2 target에서 관측한 실제 multi-view 데이터다. +- paired case는 source와 target이 같은 원본 `time_id`를 갖도록 정렬된다. +- 20개 case 중 13개만 paired target RGB를 가지며, 나머지 7개는 원본 validation camera가 없는 source-only case다. + +즉 DyCheck는 dataset-level로는 `has_target=true`이지만 target coverage가 `mixed`다. loader는 데이터셋 이름이나 디렉터리 존재를 추측하지 말고, 각 case의 `target_path`와 `pair_id`를 확인해야 한다. + +### 2.3 Multi-view, novel-time video + +- **CamXTime**은 scene별 120-camera × 120-time RGB grid를 기반으로 한다. +- 하나의 `forward` source에 camera 경로와 원본 motion-frame mapping이 다른 7개 target을 연결한다. +- target pattern은 `backward`, `zigzag`, `slowmo`, `fastmo`, `bullettime_early`, `bullettime_mid`, `bullettime_late`다. + +source와 target의 출력 index는 모두 0–80이지만, 같은 index가 같은 원본 시간을 뜻하지 않는다. 정확한 관계는 양쪽 `extras/temporal_mapping.json`에 기록된 camera index와 motion-frame index로 해석해야 한다. 이 특성 때문에 CamXTime은 단순 novel-view가 아니라 **novel-view + novel-time** 평가 데이터다. + +## 3. 데이터셋별 상세 설명 + +### 3.1 DynPose (`dynpose_eval`) + +#### 성격과 목적 + +DynPose-100K++에서 `num_frames >= 81` 조건으로 선택한 400개 표본 중 RGB를 복원할 수 있었던 377개를 포함한다. 영상은 metadata의 YouTube 링크와 시간 구간을 이용해 복원했으며, 23개 private/unavailable 영상은 최종 set에서 제외했다. + +이 데이터셋은 자연스러운 촬영 조건, 압축, camera motion, 동적 객체가 섞인 대규모 in-the-wild 단안 평가군이다. 실제 paired target은 없으며, ViPE 관측 camera 위에 NeoVerse 기본 12종 relative path를 합성해 camera-control-only benchmark를 구성한다. + +#### 구성 + +- 고유 source: 377개 +- 평가 case: 4,524개 (`377 × 12 trajectories`) +- 총 source frame: 59,849개 +- camera-control pose: 718,188개 (`59,849 × 12`) +- scene별 길이: 83–256프레임 +- FPS: 23.976, 24, 25, 29.97, 30 등을 포함한 10개 rational variant +- RGB: H.264 MP4, `yuv420p`, `832×480` +- target RGB: 없음 +- camera control: `pan_left/right`, `tilt_up/down`, `move_left/right`, `push_in/pull_out`, `boom_up/down`, `orbit_left/right` +- pairing: 4,524개 모두 camera-control-only unpaired case + +#### 카메라와 부가 데이터 + +- pose는 ViPE 공식 artifact의 OpenCV `c2w`다. +- intrinsics와 pose 원본 NPZ를 `extras/`에 보존한다. +- Z-only half-float OpenEXR depth를 scene별 ZIP으로 보존한다. 이 depth는 원본 canvas 기준이며 핵심 RGB처럼 crop/resize된 값이 아니다. +- 한 scene은 `fx`, `fy`가 모두 음수여서, projection을 보존하도록 focal sign과 pose를 함께 정규화했다. +- camera translation은 물리 단위가 확정되지 않은 reconstruction unit이다. + +#### Camera-control 정의 + +- 각 trajectory는 source와 같은 83–256프레임의 relative OpenCV `c2w`다. +- frame 0의 identity에서 마지막 지시 pose까지 rotation Slerp와 translation 선형 보간을 사용한다. +- 프레임별 `target_c2w[t] = source_c2w[t] @ relative_c2w[t]`로 absolute control을 만든다. +- pan/tilt는 15°, translation은 0.1 reconstruction unit, orbit은 15°와 radius 1.0의 NeoVerse 기본값이다. + +#### 주의점 + +- source pose는 목표 trajectory가 아니라 관측 RGB의 추정 trajectory다. +- 합성 distance/radius는 물리 미터가 아니며 scene 사이의 절대 이동량으로 비교하지 않는다. +- YouTube timestamp만으로 FPS를 재추론하지 말고 case의 `fps`와 `output_fps_rational`을 사용해야 한다. +- 원래 400개 표본 전체가 아니라 RGB 복원에 성공한 377개가 최종 모집단이다. + +### 3.2 SpatialVID (`spatialvid_eval`) + +#### 성격과 목적 + +SpatialVID에서 동적 콘텐츠를 중심으로 선정한 200개 단안 영상 평가군이다. 원본 영상 전체가 아니라 annotation 구간에서 균등 추출된 81개 frame을 사용한다. 도시, 거리, 군중, 이동 촬영처럼 실제 영상의 장면 다양성과 동적 복잡도를 분석하기 좋다. 각 source에 NeoVerse demo와 동일한 12개 camera instruction을 결합해 camera-control 평가 case를 정의한다. + +#### 구성 + +- 고유 source: 200개 +- 평가 case: 2,400개 (`200 × 12 trajectories`) +- 총 source frame: 16,200개 +- camera-control pose: 194,400개 (`2,400 × 81`) +- 길이: 모든 case가 81프레임 +- RGB: lossless PNG sequence, `832×480` +- target RGB: 없음 +- camera control: `pan_left/right`, `tilt_up/down`, `move_left/right`, `push_in/pull_out`, `boom_up/down`, `orbit_left/right` +- pairing: 2,400개 모두 camera-control-only unpaired case +- dynamic mask: 없음 + +#### Camera-control 정의 + +- NeoVerse `CameraTrajectory.from_predefined`의 기본값을 사용한다. pan/tilt는 15°, translation은 0.1 reconstruction unit, orbit은 15°와 radius 1.0이다. +- 모든 trajectory는 81프레임의 relative OpenCV `c2w`이며, frame 0의 identity에서 frame 80의 지시 pose까지 rotation Slerp와 translation 선형 보간을 사용한다. +- SpatialVID는 동적 source이므로 NeoVerse demo와 동일하게 프레임별 `target_c2w[t] = source_c2w[t] @ relative_c2w[t]`로 absolute control을 만든다. 따라서 모든 control의 첫 pose는 해당 source 첫 pose와 정확히 같다. +- NeoVerse-compatible keyframe 정의는 `trajectory_specs/.json`, 통합 포맷의 absolute control은 `camera_controls///transforms.json`에 저장한다. +- orbit radius 1.0은 물리 미터가 아니라 SpatialVID source reconstruction unit이다. scene 사이의 절대 이동량으로 해석하지 않는다. + +#### 시간 표현 + +입력 자체가 원본에서 균등 subsampling된 PNG sequence이므로 하나의 출력 FPS를 부여하지 않는다. 각 frame의 정확한 원본 index와 `source_frame_index / original_video_fps`로 계산한 timestamp는 `extras/temporal_mapping.json`에 있다. 따라서 일정 간격의 81개 배열이라는 사실을 81-frame짜리 원본 동영상으로 오해하면 안 된다. + +#### 분석 가능한 metadata + +각 case에는 다음과 같은 clustering feature가 있다. + +- `dynamic_ratio` +- `crowd_density` +- `motion_tags` +- `scene_type` +- `aesthetic_score` +- `camera_move_distance` +- `camera_rotation_angle` +- 원본 video FPS와 frame 수 + +이 필드들은 움직임 크기, 군중 밀도, scene domain, camera translation/rotation 강도에 따른 stratified evaluation에 바로 사용할 수 있다. + +#### 주의점 + +- `fps=null`은 FPS 정보가 없다는 뜻이 아니라, subsampling 결과 전체를 표현하는 단일 FPS가 부정확하다는 뜻이다. +- RGB와 같은 시점의 pose는 원래 관측 카메라이고, 목표 path는 위 규칙으로 합성한 benchmark control이다. 둘을 실제 별도 촬영 camera로 해석하면 안 된다. +- camera translation의 물리 단위는 확정되지 않았다. + +### 3.3 Vista4D (`vista4d_eval`) + +#### 성격과 목적 + +Pexels와 DAVIS의 in-the-wild 단안 source에 사전에 정의된 novel camera trajectory를 결합한 camera-control 평가군이다. 입력 영상과 원하는 camera motion은 있지만, 그 camera에서 촬영된 실제 target RGB는 없다. + +#### 구성 + +- 고유 source: 51개 + - Pexels 38개 + - DAVIS 13개 +- 평가 case/camera trajectory: 110개 +- source frame: 51 × 49 = 2,499개 +- camera-control pose: 110 × 49 = 5,390개 +- FPS 분포: source 기준 24 FPS 19개, 25 FPS 23개, 30 FPS 9개 +- RGB: H.264 MP4, `832×480` +- target: 없음 + +#### 카메라와 마스크 + +- `camera_controls///transforms.json`이 실제 평가 control이다. +- source camera rotation은 원본의 작은 SO(3) 오차를 SVD로 가장 가까운 proper rotation에 projection했다. translation은 바꾸지 않았다. +- 표준 first-frame 일치 조건을 만족한 FSFF case는 1개뿐이다. 나머지 109개 원본 FSFF 배열은 표준 control로 선언하지 않고 `extras/original_fsff.npz`에 보존한다. +- 51개 source 모두 dynamic/background mask가 있으며 `background == ~dynamic`이다. +- sky mask는 background mask와 다른 의미이므로 `extras/sky_masks.npz`에 분리돼 있다. + +#### 적합한 평가 + +- camera-conditioned video generation +- trajectory adherence +- source 첫 frame 또는 source video의 내용/동작 보존 +- camera motion 종류별 성능 분석 + +실제 target RGB가 없으므로 pixel-aligned full-reference metric을 바로 계산할 수는 없다. + +### 3.4 DyCheck (`dycheck_eval`) + +#### 성격과 목적 + +동적 장면을 여러 실제 카메라로 촬영한 multi-view 평가군이다. camera 0의 train frame을 source로, camera 1 또는 2의 validation frame을 target으로 사용하며 동일한 원본 time ID끼리 pair를 구성한다. + +#### 구성 + +- 원본 scene: 20개 +- 평가 case: 20개 +- paired case: 13개 +- source-only case: 7개 +- source frame: 5,783개 +- target frame: 3,928개 +- 총 RGB frame: 9,711개 +- scene/camera별 길이: 180–424프레임 +- FPS: 18개 case가 30, 2개 case가 60 +- RGB: PNG sequence, `832×480` + +paired scene은 `apple`, `block`, `paper-windmill`, `space-out`, `spin`, `teddy`, `wheel`에서 구성되며 일부 scene은 camera 1과 camera 2가 각각 별도 case다. `backpack`, `creeper`, `handwavy`, `haru-sit`, `mochi-high-five`, `pillow`, `sriracha-tree`는 paired validation RGB가 없어 source-only다. + +#### 카메라, 시간, 마스크 + +- 원본 camera `orientation`의 transpose와 camera `position`으로 OpenCV `c2w`를 구성한다. +- bad-frame 제거 때문에 source/target frame 수와 시간 간격이 camera별로 다를 수 있다. +- 정확한 원본 frame name, camera ID, time ID는 `extras/frame_mapping.json`에 있다. +- 원본 RGBA alpha는 dynamic mask가 아니라 RGB/LiDAR projection의 valid mask다. +- target의 co-visible mask는 novel-view metric 유효영역이며 dynamic/background mask가 아니다. + +#### 주의점 + +- `protocol.has_target=true`만 보고 모든 case에 target이 있다고 가정하면 안 된다. +- paired frame은 배열을 단순 연결한 것이 아니라 같은 원본 time ID로 매칭한 결과다. +- portrait source가 많아 중앙 crop 범위가 크므로 원본과 시각적 field of view가 다를 수 있다. + +### 3.5 CamXTime (`camxtime_eval`) + +#### 성격과 목적 + +camera와 time을 동시에 제어하는 평가군이다. scene별 camera-time grid에서 `forward` source와 서로 다른 시간 진행 방식의 target을 실제 RGB로 조립한다. 다섯 데이터셋 중 유일하게 **모든 case가 paired target을 가지며**, target 시간이 source와 의도적으로 달라질 수 있다. + +#### 구성 + +- 원래 scene: 32개 +- 최종 포함 scene: 31개 +- 제외 scene: `scene_25` 1개 (`camera_039.mp4` 누락) +- source: 31개 +- target 및 paired case: 217개 = 31 scene × 7 pattern +- source frame: 2,511개 +- target frame: 17,577개 +- 모든 sequence: 81프레임, 30 FPS, `832×480` +- RGB: H.264 MP4, `yuv420p` + +#### 7개 target temporal pattern + +| Pattern | 의미 | +|---|---| +| `backward` | 원본 동작 시간을 역방향으로 진행 | +| `zigzag` | 시간축을 앞뒤로 오가는 mapping | +| `slowmo` | 느린 시간 진행 | +| `fastmo` | 빠른 시간 진행 | +| `bullettime_early` | 원본 motion frame 30에 시간을 고정 | +| `bullettime_mid` | 원본 motion frame 60에 시간을 고정 | +| `bullettime_late` | 원본 motion frame 90에 시간을 고정 | + +#### 카메라와 시간 mapping + +- authoritative mapping은 전처리 metadata의 `source_cam_idx`, `source_motion_frame`, `timestamps`다. +- source와 target은 출력 index로는 대응하지만 원본 motion time은 pattern별로 다르다. +- `slowmo`와 `fastmo`는 helper 구현만 보면 같아 보이지만, 실제 eval metadata의 motion-frame mapping은 서로 다르므로 metadata를 우선한다. +- camera rotation의 작은 비정규성은 translation을 유지한 채 SVD로 proper rotation에 projection했다. +- intrinsics는 이미 `832×480` image plane용이므로 RGB crop/resize 변환을 다시 적용하지 않는다. + +#### 주의점 + +- 누락 camera를 이웃 camera RGB로 대체하면 선언 pose와 영상이 불일치하므로 `scene_25`는 제외됐다. +- source와 target의 frame index가 같다고 동일 시간이라고 판단하면 안 된다. +- dynamic mask는 제공되지 않는다. + +## 4. 추가 clustering 축 + +세 가지 주 분류 외에도 아래 축으로 데이터셋 또는 case를 묶으면 평가 결과를 더 잘 해석할 수 있다. + +### 4.1 평가 supervision 형태 + +| 군집 | 데이터셋 | 평가 신호 | +|---|---|---| +| Full paired RGB | CamXTime | 모든 case에 target RGB 존재 | +| Mixed paired RGB | DyCheck | 13 paired + 7 source-only | +| Camera-control-only | DynPose, SpatialVID, Vista4D | 목표 pose는 있지만 target RGB 없음 | + +이 구분은 metric 선택에 직접 영향을 준다. paired RGB 군집은 full-reference metric을 사용할 수 있지만, camera-control-only/source-only 군집은 trajectory adherence, geometry consistency, perceptual quality처럼 target RGB에 의존하지 않는 평가가 필요하다. + +### 4.2 시간 구조 + +| 시간 군집 | 데이터셋 | 특징 | +|---|---|---| +| 원본 진행 유지 | DynPose, Vista4D | 원본 frame 수와 FPS 유지 | +| 원본 time ID 동기화 | DyCheck | 서로 다른 카메라를 동일 time ID로 pair | +| 균등 subsampling | SpatialVID | 81개 정수 frame index, 단일 출력 FPS 미정의 | +| 의도적 retiming | CamXTime | reverse, zigzag, speed change, frozen time | + +### 4.3 카메라의 출처 + +| 카메라 군집 | 데이터셋 | 해석 | +|---|---|---| +| 실제 multi-camera capture | DyCheck | 서로 다른 실제 촬영 camera | +| camera-time RGB grid | CamXTime | grid에서 camera/time index를 선택 | +| reconstruction/estimation camera | DynPose, SpatialVID source | 관측 RGB에 추정·제공된 trajectory | +| 설계된 novel camera control | DynPose/SpatialVID control, Vista4D | source에 합성하거나 별도로 주어진 목표 trajectory | + +### 4.4 동적 콘텐츠 및 장면 다양성 + +- **광범위한 in-the-wild 다양성**: DynPose, SpatialVID +- **동적 객체 중심의 소규모 정밀 multi-view capture**: DyCheck +- **camera motion prompt/trajectory 다양성**: Vista4D +- **시간 조작 다양성**: CamXTime + +특히 DynPose/SpatialVID는 12개 표준 trajectory family를 제공하며, SpatialVID는 여기에 `dynamic_ratio`, crowd density, scene type, motion tag를 함께 제공해 움직임·장면 복잡도·제어 방향별 clustering에 적합하다. Vista4D는 trajectory 이름을 기준으로 dolly, crane, arc, follow, zoom 계열을 분리할 수 있고, CamXTime은 temporal pattern이 명시적인 실험 축이다. + +### 4.5 부가 신호 + +| 데이터셋 | Dynamic/background mask | 기타 mask | Depth | 정확한 temporal mapping | +|---|---|---|---|---| +| DynPose | 없음 | 없음 | Z-only EXR ZIP | YouTube 구간/FPS metadata | +| SpatialVID | 없음 | 없음 | 없음 | 원본 frame index와 timestamp | +| Vista4D | 있음 | sky mask | 없음 | 원본 FPS 유지 | +| DyCheck | 없음 | valid mask, target co-visible mask | 최종 포맷에는 미포함 | frame name/time ID/camera ID | +| CamXTime | 없음 | 없음 | 없음 | camera index/motion-frame/timestamp | + +마스크 이름이 비슷해도 의미가 다르다. Vista4D의 dynamic/background만 표준 foreground/complement 관계이며, DyCheck alpha와 co-visible mask는 metric 유효영역 계열이다. + +### 4.6 Caption 출처 + +- **원본 metadata caption 재사용**: DynPose, Vista4D, CamXTime +- **Qwen3-VL로 생성**: SpatialVID, DyCheck +- **target별 시간 의미를 반영한 caption**: CamXTime bullet-time target +- **동기화된 시점 간 source caption 재사용**: DyCheck paired target + +caption 출처가 다르므로 text-video alignment를 비교할 때 생성 caption과 원본 caption을 별도 군집으로 분석하는 것이 안전하다. + +### 4.7 저장 표현과 decode 비용 + +- **MP4**: DynPose, Vista4D, CamXTime +- **PNG sequence**: SpatialVID, DyCheck + +PNG sequence는 frame-exact 접근과 반복 실험에 유리하지만 inode와 저장 공간 사용량이 크다. MP4는 저장 효율이 높지만 random access 및 decode 방식이 평가 속도와 재현성에 영향을 줄 수 있다. + +## 5. 평가 설계 권장안 + +### 5.1 최소 공통 평가 + +5개 데이터셋에 공통으로 적용할 수 있는 축은 다음과 같다. + +- 출력 해상도 `480×832` +- OpenCV pinhole `c2w` +- source caption 조건 +- temporal consistency +- camera/geometry consistency +- perceptual quality + +단, 모든 데이터셋에 target RGB가 있는 것은 아니므로 pixel metric을 공통 필수 metric으로 두면 안 된다. + +### 5.2 계층형 결과 보고 + +전체 평균 하나보다는 다음 순서로 결과를 나눠 보고하는 것이 좋다. + +1. 주 카테고리: monocular / multi-view / multi-view novel-time +2. supervision: paired RGB / mixed pair / camera-only / source-only +3. 시간 구조: original / synchronized / subsampled / retimed +4. camera motion 강도 또는 trajectory family +5. 동적 복잡도와 scene domain + +이 구조는 217-case CamXTime이나 377-case DynPose처럼 큰 데이터셋이 전체 평균을 과도하게 지배하는 문제도 줄여준다. 데이터셋별 평균을 먼저 계산한 뒤 카테고리 평균을 구하는 macro-average와, 전체 case를 합산하는 micro-average를 함께 보고하는 것이 권장된다. + +## 6. Loader 구현 시 핵심 규칙 + +- 경로와 관계는 디렉터리 이름으로 추론하지 말고 각 `manifest.json`의 `cases`와 `pairing`을 사용한다. +- `target_path != null`일 때만 paired target을 읽는다. +- `camera_control != null`은 실제 target RGB가 아니라 목표 camera trajectory다. +- `protocol.num_frames` 또는 `protocol.fps`가 `null`이면 case별 값을 확인한다. +- SpatialVID와 CamXTime의 시간 관계는 `extras/temporal_mapping.json`을 사용한다. +- DyCheck pair는 source/target의 원본 time ID mapping을 확인한다. +- mask는 이름이 아니라 manifest extension의 의미를 확인한다. +- camera translation의 물리 단위가 `unspecified`인 데이터셋 사이에서 절대 이동량을 직접 비교하지 않는다. + +## 7. Manifest 위치 + +- [`camxtime_eval/manifest.json`](camxtime_eval/manifest.json) +- [`dycheck_eval/manifest.json`](dycheck_eval/manifest.json) +- [`dynpose_eval/manifest.json`](dynpose_eval/manifest.json) +- [`spatialvid_eval/manifest.json`](spatialvid_eval/manifest.json) +- [`vista4d_eval/manifest.json`](vista4d_eval/manifest.json) diff --git a/build_camera_controls.py b/build_camera_controls.py new file mode 100644 index 0000000000000000000000000000000000000000..0ecc9295e9dfa0006c1fb3c12c268846f48c8872 --- /dev/null +++ b/build_camera_controls.py @@ -0,0 +1,59 @@ +"""Build and validate the unified 12-control DynPose and SpatialVID sets.""" + +from __future__ import annotations + +import json +import subprocess +from datetime import datetime, timezone +from pathlib import Path + + +ROOT = Path(__file__).resolve().parent +STATUS = ROOT / "camera_controls_build_status.json" +LOG = ROOT / "camera_controls_build.log" +BUILDERS = ( + ROOT / "spatialvid_eval/build_camera_controls.py", + ROOT / "dynpose_eval/build_camera_controls.py", +) + + +def now() -> str: + return datetime.now(timezone.utc).isoformat() + + +def write_status(payload: dict) -> None: + STATUS.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8") + + +def main() -> None: + report: dict[str, object] = {"status": "running", "started_at": now(), "datasets": {}} + write_status(report) + with LOG.open("w", encoding="utf-8") as log: + for builder in BUILDERS: + dataset = builder.parent.name + process = subprocess.run( + ["/opt/conda/bin/python", str(builder)], + cwd=builder.parent, + text=True, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + ) + log.write(f"===== {dataset} =====\n{process.stdout}\n") + log.flush() + item: dict[str, object] = {"returncode": process.returncode} + try: + item["validation"] = json.loads(process.stdout) + except json.JSONDecodeError: + item["output_tail"] = process.stdout[-4000:] + report["datasets"][dataset] = item # type: ignore[index] + if process.returncode: + report.update({"status": "failed", "finished_at": now()}) + write_status(report) + raise SystemExit(process.returncode) + report.update({"status": "complete", "finished_at": now()}) + write_status(report) + print(json.dumps(report, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/camera_controls_build.log b/camera_controls_build.log new file mode 100644 index 0000000000000000000000000000000000000000..22857afefa5d0af4c01ac1b62b4979cf66b4cedb --- /dev/null +++ b/camera_controls_build.log @@ -0,0 +1,51 @@ +===== spatialvid_eval ===== +{ + "scenes": 200, + "cases": 2400, + "trajectories": [ + "pan_left", + "pan_right", + "tilt_up", + "tilt_down", + "move_left", + "move_right", + "push_in", + "pull_out", + "boom_up", + "boom_down", + "orbit_left", + "orbit_right" + ], + "max_first_frame_pose_error": 0.0, + "max_composition_error": 0.0, + "max_rotation_orthonormal_error": 1.3322676295501878e-15, + "max_rotation_determinant_error": 1.9984014443252818e-15 +} + +===== dynpose_eval ===== +{ + "scenes": 377, + "cases": 4524, + "trajectories": [ + "pan_left", + "pan_right", + "tilt_up", + "tilt_down", + "move_left", + "move_right", + "push_in", + "pull_out", + "boom_up", + "boom_down", + "orbit_left", + "orbit_right" + ], + "camera_control_frames": 718188, + "min_frames": 83, + "max_frames": 256, + "max_first_frame_pose_error": 0.0, + "max_composition_error": 0.0, + "max_rotation_orthonormal_error": 7.203480951734065e-07, + "max_rotation_determinant_error": 6.667323937392666e-07 +} + diff --git a/camera_controls_build_status.json b/camera_controls_build_status.json new file mode 100644 index 0000000000000000000000000000000000000000..f6fdabbd475d1d11bee79e292ff6351ce48b6a8c --- /dev/null +++ b/camera_controls_build_status.json @@ -0,0 +1,60 @@ +{ + "status": "complete", + "started_at": "2026-08-30T22:20:29.632192+00:00", + "datasets": { + "spatialvid_eval": { + "returncode": 0, + "validation": { + "scenes": 200, + "cases": 2400, + "trajectories": [ + "pan_left", + "pan_right", + "tilt_up", + "tilt_down", + "move_left", + "move_right", + "push_in", + "pull_out", + "boom_up", + "boom_down", + "orbit_left", + "orbit_right" + ], + "max_first_frame_pose_error": 0.0, + "max_composition_error": 0.0, + "max_rotation_orthonormal_error": 1.3322676295501878e-15, + "max_rotation_determinant_error": 1.9984014443252818e-15 + } + }, + "dynpose_eval": { + "returncode": 0, + "validation": { + "scenes": 377, + "cases": 4524, + "trajectories": [ + "pan_left", + "pan_right", + "tilt_up", + "tilt_down", + "move_left", + "move_right", + "push_in", + "pull_out", + "boom_up", + "boom_down", + "orbit_left", + "orbit_right" + ], + "camera_control_frames": 718188, + "min_frames": 83, + "max_frames": 256, + "max_first_frame_pose_error": 0.0, + "max_composition_error": 0.0, + "max_rotation_orthonormal_error": 7.203480951734065e-07, + "max_rotation_determinant_error": 6.667323937392666e-07 + } + } + }, + "finished_at": "2026-08-30T22:21:24.535660+00:00" +} diff --git a/spatialvid_eval/CAMERA_CONTROLS.md b/spatialvid_eval/CAMERA_CONTROLS.md new file mode 100644 index 0000000000000000000000000000000000000000..9150627f4d64274a91d5d692cc14b4bb25b2ec18 --- /dev/null +++ b/spatialvid_eval/CAMERA_CONTROLS.md @@ -0,0 +1,52 @@ +# SpatialVID synthetic camera controls + +SpatialVID 원본은 관측 RGB와 그 RGB에 대응하는 source camera만 제공한다. +이 평가판의 novel camera paths는 실제 별도 촬영 camera가 아니라 NeoVerse +demo 규약을 이용해 명시적으로 합성한 benchmark instructions다. + +## Twelve instructions + +| trajectory ID | NeoVerse endpoint operation | relative endpoint translation | +|---|---|---| +| `orbit_left` | `angle=15`, `orbit_radius=1.0` | `[-0.258819, 0, 0.034074]` | +| `orbit_right` | `angle=15`, `orbit_radius=1.0` | `[+0.258819, 0, 0.034074]` | +| `tilt_up` | `angle=15` | `[0, 0, 0]` | +| `tilt_down` | `angle=15` | `[0, 0, 0]` | +| `pan_left` | `angle=15` | `[0, 0, 0]` | +| `pan_right` | `angle=15` | `[0, 0, 0]` | +| `move_left` | `distance=0.1` | `[-0.1, 0, 0]` | +| `move_right` | `distance=0.1` | `[+0.1, 0, 0]` | +| `push_in` | `distance=0.1` | `[0, 0, +0.1]` | +| `pull_out` | `distance=0.1` | `[0, 0, -0.1]` | +| `boom_up` | `distance=0.1` | `[0, -0.1, 0]` | +| `boom_down` | `distance=0.1` | `[0, +0.1, 0]` | + +각 path는 `num_frames=81`, `mode="relative"`, `zoom_ratio=1.0`, +`use_first_frame=true`다. Frame 0은 identity이고 frame 80은 위 endpoint다. +NeoVerse `CameraTrajectory._compose_keyframes`와 동일하게 rotation은 Slerp, +translation은 선형 보간한다. 좌우/상하 부호는 일반적인 이름 추측이 아니라 +NeoVerse `_get_camera_matrix` 구현을 그대로 따른다. + +SpatialVID는 dynamic scene으로 취급하므로 각 frame의 absolute control은 다음과 같다. + +```text +target_c2w[t] = source_c2w[t] @ relative_c2w[t] +``` + +따라서 target frame 0은 source frame 0과 정확히 같고, 원래 관측 camera +motion 위에 지시 motion이 합성된다. 모든 행렬은 OpenCV camera-to-world다. +Orbit radius 1.0과 translation distance 0.1은 물리 미터가 아니라 source +reconstruction unit이다. + +## Files + +- NeoVerse-compatible relative keyframes: + `trajectory_specs/.json` +- 평가 loader가 읽는 absolute OpenCV c2w: + `camera_controls///transforms.json` +- Case ID: `__` +- 재생성 및 검증: `python build_camera_controls.py` +- 읽기 전용 검증: `python build_camera_controls.py --check` + +200개 source 각각에 12개 path를 연결하므로 manifest에는 2,400개 +camera-control-only case와 194,400개 control pose가 있다. Paired target RGB는 없다. diff --git a/spatialvid_eval/build_camera_controls.py b/spatialvid_eval/build_camera_controls.py new file mode 100644 index 0000000000000000000000000000000000000000..f2fac0dc59798321f0c59cb1780f11b9457e3a1a --- /dev/null +++ b/spatialvid_eval/build_camera_controls.py @@ -0,0 +1,364 @@ +"""Build twelve NeoVerse-style camera controls for every SpatialVID scene. + +The relative trajectories reproduce ``CameraTrajectory.from_predefined`` in +NeoVerse's ``diffsynth/utils/auxiliary.py``. SpatialVID is a dynamic source, +so the NeoVerse demo composition is applied frame by frame: + + target_c2w[t] = source_c2w[t] @ relative_c2w[t] + +The emitted controls use the unified dataset's absolute OpenCV c2w schema. +""" + +from __future__ import annotations + +import argparse +import json +from copy import deepcopy +from pathlib import Path +from typing import Any + +import numpy as np +from scipy.spatial.transform import Rotation, Slerp + + +ROOT = Path(__file__).resolve().parent +NUM_FRAMES = 81 +LAST_FRAME = NUM_FRAMES - 1 +CONTROL_SPECS: dict[str, dict[str, float]] = { + "pan_left": {"angle": 15.0}, + "pan_right": {"angle": 15.0}, + "tilt_up": {"angle": 15.0}, + "tilt_down": {"angle": 15.0}, + "move_left": {"distance": 0.1}, + "move_right": {"distance": 0.1}, + "push_in": {"distance": 0.1}, + "pull_out": {"distance": 0.1}, + "boom_up": {"distance": 0.1}, + "boom_down": {"distance": 0.1}, + "orbit_left": {"angle": 15.0, "orbit_radius": 1.0}, + "orbit_right": {"angle": 15.0, "orbit_radius": 1.0}, +} +COMPOSITION = "target_c2w[t] = source_c2w[t] @ relative_c2w[t]" +PROVENANCE = ( + "NeoVerse CameraTrajectory.from_predefined defaults and dynamic-scene " + "relative composition" +) + + +def relative_endpoint(name: str, params: dict[str, float]) -> np.ndarray: + """Match NeoVerse CameraTrajectory._get_camera_matrix exactly.""" + angle = np.deg2rad(params.get("angle", 30.0)) + radius = params.get("orbit_radius", 0.5) + pose = np.eye(4, dtype=np.float64) + if name == "move_left": + pose[0, 3] = -params["distance"] + elif name == "move_right": + pose[0, 3] = params["distance"] + elif name == "push_in": + pose[2, 3] = params["distance"] + elif name == "pull_out": + pose[2, 3] = -params["distance"] + elif name == "boom_up": + pose[1, 3] = -params["distance"] + elif name == "boom_down": + pose[1, 3] = params["distance"] + elif name in {"orbit_left", "orbit_right"}: + theta = -angle if name == "orbit_left" else angle + cosine, sine = np.cos(theta), np.sin(theta) + pose[0, 3] = radius * sine + pose[2, 3] = radius * (1.0 - cosine) + pose[0, 0] = cosine + pose[0, 2] = -sine + pose[2, 0] = sine + pose[2, 2] = cosine + elif name in {"pan_left", "pan_right"}: + theta = -angle if name == "pan_left" else angle + cosine, sine = np.cos(theta), np.sin(theta) + pose[0, 0] = cosine + pose[0, 2] = sine + pose[2, 0] = -sine + pose[2, 2] = cosine + elif name in {"tilt_up", "tilt_down"}: + theta = angle if name == "tilt_up" else -angle + cosine, sine = np.cos(theta), np.sin(theta) + pose[1, 1] = cosine + pose[1, 2] = -sine + pose[2, 1] = sine + pose[2, 2] = cosine + else: + raise ValueError(f"unsupported control: {name}") + return pose + + +def relative_trajectory(name: str, params: dict[str, float]) -> np.ndarray: + """Match NeoVerse's two-keyframe rotation Slerp and linear translation.""" + endpoint = relative_endpoint(name, params) + frame_times = np.linspace(0.0, 1.0, NUM_FRAMES) + rotations = Slerp( + [0.0, 1.0], + Rotation.from_matrix(np.stack([np.eye(3), endpoint[:3, :3]])), + )(frame_times).as_matrix() + positions = frame_times[:, None] * endpoint[:3, 3][None] + output = np.repeat(np.eye(4, dtype=np.float64)[None], NUM_FRAMES, axis=0) + output[:, :3, :3] = rotations + output[:, :3, 3] = positions + return output + + +def trajectory_spec(name: str, params: dict[str, float]) -> dict[str, Any]: + json_params = { + key: int(value) if key == "angle" and float(value).is_integer() else value + for key, value in params.items() + } + return { + "name": name, + "description": f"NeoVerse predefined {name} control for SpatialVID", + "mode": "relative", + "num_frames": NUM_FRAMES, + "zoom_ratio": 1.0, + "use_first_frame": True, + "composition": COMPOSITION, + "keyframes": [ + {"0": [{"static": {}}]}, + {str(LAST_FRAME): [{name: json_params}]}, + ], + } + + +def source_records(manifest: dict[str, Any]) -> list[dict[str, Any]]: + """Recover one authoritative source record per scene, also after reruns.""" + records: dict[str, dict[str, Any]] = {} + for case in manifest["cases"]: + scene = case["scene"] + if scene in records: + continue + base = deepcopy(case) + base["case_id"] = scene + base["traj"] = None + base["target_path"] = None + base["camera_control"] = None + base["pair_id"] = None + metadata = deepcopy(base.get("metadata", {})) + metadata.pop("camera_control_provenance", None) + base["metadata"] = metadata + records[scene] = base + return [records[key] for key in sorted(records)] + + +def control_document(source: dict[str, Any], relative: np.ndarray) -> dict[str, Any]: + frames = source["frames"] + if len(frames) != NUM_FRAMES: + raise ValueError(f"expected {NUM_FRAMES} source frames, got {len(frames)}") + source_c2w = np.asarray([frame["c2w"] for frame in frames], dtype=np.float64) + target_c2w = source_c2w @ relative + return { + "format_version": source["format_version"], + "camera_model": source["camera_model"], + "pose_type": source["pose_type"], + "coordinate_system": source["coordinate_system"], + "image_hw": source["image_hw"], + "num_frames": NUM_FRAMES, + "frames": [ + { + "frame_index": index, + "c2w": target_c2w[index].tolist(), + "intrinsics": deepcopy(frames[index]["intrinsics"]), + } + for index in range(NUM_FRAMES) + ], + } + + +def controlled_case(base: dict[str, Any], name: str, params: dict[str, float]) -> dict[str, Any]: + scene = base["scene"] + case = deepcopy(base) + case["case_id"] = f"{scene}__{name}" + case["traj"] = name + case["target_path"] = None + case["camera_control"] = { + "path": f"camera_controls/{scene}/{name}/transforms.json", + "variant": "default", + } + case["pair_id"] = None + case["metadata"]["camera_control_provenance"] = { + "source": PROVENANCE, + "spec_path": f"trajectory_specs/{name}.json", + "mode": "relative", + "composition": COMPOSITION, + "angle_degrees": params.get("angle"), + "distance": params.get("distance"), + "distance_unit": "source_reconstruction_unit" if "distance" in params else None, + "orbit_radius": params.get("orbit_radius"), + "orbit_radius_unit": "source_reconstruction_unit" if "orbit_radius" in params else None, + } + return case + + +def update_manifest(manifest: dict[str, Any], bases: list[dict[str, Any]]) -> dict[str, Any]: + output = deepcopy(manifest) + cases = [ + controlled_case(base, name, params) + for base in bases + for name, params in CONTROL_SPECS.items() + ] + output["protocol"]["control"] = "camera" + output["camera"].update({ + "default_control_variant": "default", + "synthetic_control_mode": "relative", + "synthetic_control_composition": COMPOSITION, + }) + output["extensions"]["camera_trajectory_specs"] = { + "path": "trajectory_specs/.json", + "meaning": "NeoVerse-compatible relative keyframe trajectory definition", + "source": PROVENANCE, + } + output["statistics"].update({ + "num_scenes": len(bases), + "num_cases": len(cases), + "num_camera_control_cases": len(cases), + "num_camera_control_frames": len(cases) * NUM_FRAMES, + "camera_control_trajectory_distribution": { + name: len(bases) for name in CONTROL_SPECS + }, + "num_pairs": 0, + "num_unpaired_cases": len(cases), + }) + output["pairing"]["unpaired_sources"] = [ + { + "case_id": case["case_id"], + "source_scene_id": case["scene"], + "source_path": case["source_path"], + "num_frames": NUM_FRAMES, + "reason": "synthetic_camera_control_only_no_target_rgb", + } + for case in cases + ] + output["cases"] = cases + return output + + +def validate(root: Path) -> dict[str, Any]: + manifest = json.loads((root / "manifest.json").read_text(encoding="utf-8")) + errors: list[str] = [] + max_first_error = 0.0 + max_composition_error = 0.0 + max_orthonormal_error = 0.0 + max_determinant_error = 0.0 + relative = { + name: relative_trajectory(name, params) + for name, params in CONTROL_SPECS.items() + } + case_ids = [case["case_id"] for case in manifest["cases"]] + control_paths = [case["camera_control"]["path"] for case in manifest["cases"]] + if len(case_ids) != len(set(case_ids)): + errors.append("duplicate case_id") + if len(control_paths) != len(set(control_paths)): + errors.append("duplicate camera control path") + for case in manifest["cases"]: + control_path = root / case["camera_control"]["path"] + source_path = root / case["source_path"] / "transforms.json" + if not control_path.is_file(): + errors.append(f"missing {control_path.relative_to(root)}") + continue + control = json.loads(control_path.read_text(encoding="utf-8")) + source = json.loads(source_path.read_text(encoding="utf-8")) + if len(control["frames"]) != NUM_FRAMES: + errors.append(f"{case['case_id']}: wrong frame count") + continue + control_c2w = np.asarray([frame["c2w"] for frame in control["frames"]]) + source_c2w = np.asarray([frame["c2w"] for frame in source["frames"]]) + max_first_error = max(max_first_error, float(np.max(np.abs(control_c2w[0] - source_c2w[0])))) + expected_c2w = source_c2w @ relative[case["traj"]] + max_composition_error = max( + max_composition_error, + float(np.max(np.abs(control_c2w - expected_c2w))), + ) + rotations = control_c2w[:, :3, :3] + orth = rotations @ np.swapaxes(rotations, 1, 2) + max_orthonormal_error = max( + max_orthonormal_error, + float(np.max(np.abs(orth - np.eye(3)[None]))), + ) + max_determinant_error = max( + max_determinant_error, + float(np.max(np.abs(np.linalg.det(rotations) - 1.0))), + ) + if not np.isfinite(control_c2w).all(): + errors.append(f"{case['case_id']}: non-finite pose") + if any(frame["intrinsics"]["fx"] <= 0 or frame["intrinsics"]["fy"] <= 0 for frame in control["frames"]): + errors.append(f"{case['case_id']}: non-positive focal length") + if [frame["frame_index"] for frame in control["frames"]] != list(range(NUM_FRAMES)): + errors.append(f"{case['case_id']}: invalid frame indexes") + if [frame["intrinsics"] for frame in control["frames"]] != [ + frame["intrinsics"] for frame in source["frames"] + ]: + errors.append(f"{case['case_id']}: intrinsics differ from source") + if len(manifest["cases"]) != len(CONTROL_SPECS) * manifest["statistics"]["num_scenes"]: + errors.append("manifest case count does not match scene × trajectory count") + if max_first_error > 1e-9: + errors.append(f"first-frame mismatch: {max_first_error}") + if max_composition_error > 1e-9: + errors.append(f"composition mismatch: {max_composition_error}") + if max_orthonormal_error > 1e-6: + errors.append(f"rotation orthonormal error: {max_orthonormal_error}") + if max_determinant_error > 1e-6: + errors.append(f"rotation determinant error: {max_determinant_error}") + if errors: + raise RuntimeError("\n".join(errors[:20])) + return { + "scenes": manifest["statistics"]["num_scenes"], + "cases": len(manifest["cases"]), + "trajectories": list(CONTROL_SPECS), + "max_first_frame_pose_error": max_first_error, + "max_composition_error": max_composition_error, + "max_rotation_orthonormal_error": max_orthonormal_error, + "max_rotation_determinant_error": max_determinant_error, + } + + +def build(root: Path) -> dict[str, Any]: + manifest_path = root / "manifest.json" + manifest = json.loads(manifest_path.read_text(encoding="utf-8")) + bases = source_records(manifest) + if len(bases) != 200: + raise ValueError(f"expected 200 SpatialVID scenes, found {len(bases)}") + spec_root = root / "trajectory_specs" + spec_root.mkdir(parents=True, exist_ok=True) + relative = {} + for name, params in CONTROL_SPECS.items(): + (spec_root / f"{name}.json").write_text( + json.dumps(trajectory_spec(name, params), indent=2) + "\n", + encoding="utf-8", + ) + relative[name] = relative_trajectory(name, params) + for base in bases: + scene = base["scene"] + source_path = root / base["source_path"] / "transforms.json" + source = json.loads(source_path.read_text(encoding="utf-8")) + for name in CONTROL_SPECS: + output_path = root / "camera_controls" / scene / name / "transforms.json" + output_path.parent.mkdir(parents=True, exist_ok=True) + output_path.write_text( + json.dumps(control_document(source, relative[name]), indent=2) + "\n", + encoding="utf-8", + ) + manifest_temp = manifest_path.with_suffix(".json.tmp") + manifest_temp.write_text( + json.dumps(update_manifest(manifest, bases), indent=2, ensure_ascii=False) + "\n", + encoding="utf-8", + ) + manifest_temp.replace(manifest_path) + return validate(root) + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--root", type=Path, default=ROOT) + parser.add_argument("--check", action="store_true") + args = parser.parse_args() + result = validate(args.root) if args.check else build(args.root) + print(json.dumps(result, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/spatialvid_eval/manifest.json b/spatialvid_eval/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1a9430e37233bb765bdcb20d4f15f38bf050acf1 --- /dev/null +++ b/spatialvid_eval/manifest.json @@ -0,0 +1,130161 @@ +{ + "name": "spatialvid_dynamic200_eval", + "format_version": "1.0", + "protocol": { + "num_frames": 81, + "latent_t": null, + "gt_hw": [ + 480, + 832 + ], + "latent_hw": null, + "metric_hw": [ + 480, + 832 + ], + "has_target": false, + "control": "camera", + "method_categories": [], + "fps": null + }, + "preprocessing": { + "spatial": "center_crop_then_resize", + "output_hw": [ + 480, + 832 + ], + "rgb_interpolation": "lanczos", + "mask_interpolation": "nearest", + "rgb_storage": "lossless_png_sequence", + "preserve_num_frames": true, + "preserve_fps": false, + "temporal_note": "The input was already an 81-frame uniform subsample; exact original source-frame indexes are preserved in the temporal_mapping extension.", + "adjust_intrinsics": true + }, + "camera": { + "model": "opencv_pinhole", + "pose_type": "c2w", + "coordinate_system": "opencv", + "intrinsics_order": [ + "fx", + "fy", + "cx", + "cy" + ], + "reconstruction_unit": "unspecified", + "default_control_variant": "default", + "synthetic_control_mode": "relative", + "synthetic_control_composition": "target_c2w[t] = source_c2w[t] @ relative_c2w[t]" + }, + "mask_encoding": { + "container": "npz", + "encoding": "numpy.packbits", + "flatten_order": "C", + "bitorder": "big", + "keys": [ + "packed", + "shape", + "bitorder" + ], + "background_definition": "logical_not(dynamic)", + "available": false, + "dynamic_masks_present": false + }, + "captioning": { + "caption_path": "source//caption.txt", + "missing_caption_model": "Qwen/Qwen3-VL-8B-Instruct", + "model_revision": "0c351dd01ed87e9c1b53cbc748cba10e6187ff3b", + "generate_only_if_missing": true, + "generation_used": true, + "frame_sampling": { + "method": "uniform_frame_indices", + "num_frames": 12, + "frame_indices": [ + 0, + 7, + 15, + 22, + 29, + 36, + 44, + 51, + 58, + 65, + 73, + 80 + ] + }, + "prompt": "Describe this video in one concise, self-contained caption. Include the main subjects, setting, actions, and meaningful temporal changes. Do not mention camera sampling, frames, or that you are an AI. Return only the caption.", + "decoding": { + "do_sample": false, + "max_new_tokens": 128 + }, + "postprocessing": { + "normalize_line_endings": true, + "strip_outer_whitespace": true, + "manual_forbidden_term_cleanup": [ + "scene_007", + "scene_062", + "scene_096" + ] + }, + "caption_key": "sha256_normalized_utf8_text" + }, + "extensions": { + "temporal_mapping": { + "path": "source//extras/temporal_mapping.json", + "meaning": "Original SpatialVID video provenance and source-frame index for each stored RGB frame" + }, + "camera_trajectory_specs": { + "path": "trajectory_specs/.json", + "meaning": "NeoVerse-compatible relative keyframe trajectory definition", + "source": "NeoVerse CameraTrajectory.from_predefined defaults and dynamic-scene relative composition" + } + }, + "statistics": { + "num_scenes": 200, + "num_cases": 2400, + "original_video_fps_distribution": { + "23.976023976023978": 3, + "24.00369794152964": 1, + "25.0": 5, + "29.97002997002997": 12, + "29.970060795814547": 1, + "30.0": 13, + "30.000000116442315": 1, + "30.00063583022722": 1, + "47.95205479452055": 1, + "50.0": 18, + "59.94005994005994": 7, + "59.94006309148265": 50, + "59.94872661550968": 1, + "59.95178433763781": 1, + "60.0": 84, + "60.00789136857503": 1 + }, + "num_pairs": 0, + "num_unpaired_cases": 2400, + "num_camera_control_cases": 2400, + "num_camera_control_frames": 194400, + "camera_control_trajectory_distribution": { + "pan_left": 200, + "pan_right": 200, + "tilt_up": 200, + "tilt_down": 200, + "move_left": 200, + "move_right": 200, + "push_in": 200, + "pull_out": 200, + "boom_up": 200, + "boom_down": 200, + "orbit_left": 200, + "orbit_right": 200 + } + }, + "pairing": { + "pair_key": "pair_id", + "scene_identity": "source_scene_id and target_scene_id identify a paired logical scene when paired target RGB exists", + "temporal_alignment": null, + "pairs": [], + "unpaired_sources": [ + { + "case_id": "scene_000__pan_left", + "source_scene_id": "scene_000", + "source_path": "source/scene_000", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_000__pan_right", + "source_scene_id": "scene_000", + "source_path": "source/scene_000", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_000__tilt_up", + "source_scene_id": "scene_000", + "source_path": "source/scene_000", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_000__tilt_down", + "source_scene_id": "scene_000", + "source_path": "source/scene_000", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_000__move_left", + "source_scene_id": "scene_000", + "source_path": "source/scene_000", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_000__move_right", + "source_scene_id": "scene_000", + "source_path": "source/scene_000", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_000__push_in", + "source_scene_id": "scene_000", + "source_path": "source/scene_000", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_000__pull_out", + "source_scene_id": "scene_000", + "source_path": "source/scene_000", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_000__boom_up", + "source_scene_id": "scene_000", + "source_path": "source/scene_000", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_000__boom_down", + "source_scene_id": "scene_000", + "source_path": "source/scene_000", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_000__orbit_left", + "source_scene_id": "scene_000", + "source_path": "source/scene_000", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_000__orbit_right", + "source_scene_id": "scene_000", + "source_path": "source/scene_000", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_001__pan_left", + "source_scene_id": "scene_001", + "source_path": "source/scene_001", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_001__pan_right", + "source_scene_id": "scene_001", + "source_path": "source/scene_001", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_001__tilt_up", + "source_scene_id": "scene_001", + "source_path": "source/scene_001", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_001__tilt_down", + "source_scene_id": "scene_001", + "source_path": "source/scene_001", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_001__move_left", + "source_scene_id": "scene_001", + "source_path": "source/scene_001", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_001__move_right", + "source_scene_id": "scene_001", + "source_path": "source/scene_001", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_001__push_in", + "source_scene_id": "scene_001", + "source_path": "source/scene_001", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_001__pull_out", + "source_scene_id": "scene_001", + "source_path": "source/scene_001", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_001__boom_up", + "source_scene_id": "scene_001", + "source_path": "source/scene_001", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_001__boom_down", + "source_scene_id": "scene_001", + "source_path": "source/scene_001", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_001__orbit_left", + "source_scene_id": "scene_001", + "source_path": "source/scene_001", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_001__orbit_right", + "source_scene_id": "scene_001", + "source_path": "source/scene_001", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_002__pan_left", + "source_scene_id": "scene_002", + "source_path": "source/scene_002", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_002__pan_right", + "source_scene_id": "scene_002", + "source_path": "source/scene_002", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_002__tilt_up", + "source_scene_id": "scene_002", + "source_path": "source/scene_002", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_002__tilt_down", + "source_scene_id": "scene_002", + "source_path": "source/scene_002", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_002__move_left", + "source_scene_id": "scene_002", + "source_path": "source/scene_002", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_002__move_right", + "source_scene_id": "scene_002", + "source_path": "source/scene_002", + "num_frames": 81, + "reason": "synthetic_camera_control_only_no_target_rgb" + }, + { + "case_id": "scene_002__push_in", + "source_scene_id": "scene_002", + "source_path": "source/scene_002", + "num_frames": 81, + "reason": 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0000000000000000000000000000000000000000..297db633c3efdc984812e31a070aeaffe216f2b5 --- /dev/null +++ b/spatialvid_eval/source/scene_035/caption.txt @@ -0,0 +1 @@ +A bustling urban street scene captures pedestrians crossing at a crosswalk as traffic lights change, with a red double-decker bus and black taxis navigating the road. Shoppers and commuters move through the area, passing storefronts like Carphone Warehouse and McDonald’s, while a graffiti-covered green utility box stands near the curb. The atmosphere is lively and dynamic, reflecting the rhythm of city life as people walk, bike, and interact amidst the backdrop of historic and modern architecture. diff --git a/spatialvid_eval/source/scene_035/extras/temporal_mapping.json b/spatialvid_eval/source/scene_035/extras/temporal_mapping.json new file mode 100644 index 0000000000000000000000000000000000000000..a79d31bfd11d90f0e3f9c070672e7803ebcdf5f1 --- /dev/null +++ b/spatialvid_eval/source/scene_035/extras/temporal_mapping.json @@ -0,0 +1,182 @@ +{ + "original_scene_id": "e6032e30-b664-568f-b57a-92d4f1383514", + "original_video_path": "videos/group_0050/e6032e30-b664-568f-b57a-92d4f1383514.mp4", + "original_video_num_frames": 360, + "original_annotation_num_frames": 90, + "source_frame_indices": [ + 0, + 4, + 9, + 13, + 18, + 22, + 27, + 31, + 36, + 40, + 44, + 49, + 53, + 58, + 62, + 67, + 71, + 76, + 80, + 85, + 89, + 93, + 98, + 102, + 107, + 111, + 116, + 120, + 125, + 129, + 134, + 138, + 142, + 147, + 151, + 156, + 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spectators enjoying the performance under the shade of trees and near café tables. diff --git a/spatialvid_eval/source/scene_102/extras/temporal_mapping.json b/spatialvid_eval/source/scene_102/extras/temporal_mapping.json new file mode 100644 index 0000000000000000000000000000000000000000..d507f2ff8e7ad0668bb6b9c3dbb93af8c8ee6ae3 --- /dev/null +++ b/spatialvid_eval/source/scene_102/extras/temporal_mapping.json @@ -0,0 +1,182 @@ +{ + "original_scene_id": "b8a1d290-1a47-519e-97c7-5a9acfe5909f", + "original_video_path": "videos/group_0054/b8a1d290-1a47-519e-97c7-5a9acfe5909f.mp4", + "original_video_num_frames": 519, + "original_annotation_num_frames": 44, + "source_frame_indices": [ + 0, + 6, + 13, + 19, + 26, + 32, + 39, + 45, + 52, + 58, + 64, + 71, + 77, + 84, + 90, + 97, + 103, + 110, + 116, + 123, + 129, + 135, + 142, + 148, + 155, + 161, + 168, + 174, + 181, + 187, + 194, + 200, + 206, + 213, + 219, + 226, + 232, + 239, + 245, + 252, + 258, + 264, + 271, + 277, + 284, + 290, + 297, 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b/spatialvid_eval/source/scene_105/caption.txt new file mode 100644 index 0000000000000000000000000000000000000000..82c79b7990ccc1ff671d32936da794d9b24523ec --- /dev/null +++ b/spatialvid_eval/source/scene_105/caption.txt @@ -0,0 +1 @@ +A vendor in an apron and gloves reaches into a large freezer to retrieve ingredients while serving customers at a bustling outdoor food stall, surrounded by people and vibrant signage. diff --git a/spatialvid_eval/source/scene_105/extras/temporal_mapping.json b/spatialvid_eval/source/scene_105/extras/temporal_mapping.json new file mode 100644 index 0000000000000000000000000000000000000000..89b2570167bd3f031e031879d4f58565fc6c38b0 --- /dev/null +++ b/spatialvid_eval/source/scene_105/extras/temporal_mapping.json @@ -0,0 +1,182 @@ +{ + "original_scene_id": "17e7cb7c-72a1-5dfc-bc84-e5693598cd87", + "original_video_path": "videos/group_0059/17e7cb7c-72a1-5dfc-bc84-e5693598cd87.mp4", + "original_video_num_frames": 288, + "original_annotation_num_frames": 24, 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b/spatialvid_eval/source/scene_112/caption.txt new file mode 100644 index 0000000000000000000000000000000000000000..c62483c51aa578879fb0ecb5c382219800be6a08 --- /dev/null +++ b/spatialvid_eval/source/scene_112/caption.txt @@ -0,0 +1 @@ +A serene urban pathway lined with blooming cherry blossom trees, bustling with pedestrians strolling under the soft pink canopy, framed by towering modern skyscrapers and a calm waterfront with boats docked, capturing the tranquil beauty of spring in a vibrant cityscape. diff --git a/spatialvid_eval/source/scene_112/extras/temporal_mapping.json b/spatialvid_eval/source/scene_112/extras/temporal_mapping.json new file mode 100644 index 0000000000000000000000000000000000000000..9e8b1ea2621ad4f9b14b0d61c3c935ffa2e7a176 --- /dev/null +++ b/spatialvid_eval/source/scene_112/extras/temporal_mapping.json @@ -0,0 +1,182 @@ +{ + "original_scene_id": "98b7739f-18c4-5dde-8cff-53b3bc979090", + "original_video_path": 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a/spatialvid_eval/source/scene_113/caption.txt b/spatialvid_eval/source/scene_113/caption.txt new file mode 100644 index 0000000000000000000000000000000000000000..a0ba5840d12ab73ee301f557a5c60bcbd82c4c35 --- /dev/null +++ b/spatialvid_eval/source/scene_113/caption.txt @@ -0,0 +1 @@ +A group of excited riders on a red roller coaster, their faces filled with joy and exhilaration as they ascend and descend through a modern, open-air structure, arms raised and laughter echoing under a cloudy sky. diff --git a/spatialvid_eval/source/scene_113/extras/temporal_mapping.json b/spatialvid_eval/source/scene_113/extras/temporal_mapping.json new file mode 100644 index 0000000000000000000000000000000000000000..7a4cf453855970eb687d8f96a01e95a70f5e46dc --- /dev/null +++ b/spatialvid_eval/source/scene_113/extras/temporal_mapping.json @@ -0,0 +1,182 @@ +{ + "original_scene_id": "6fda73c8-c214-558f-b15f-226664d7486e", + "original_video_path": "videos/group_0073/6fda73c8-c214-558f-b15f-226664d7486e.mp4", 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0000000000000000000000000000000000000000..768485313621e59ee11822c428cef0a94e2912c7 --- /dev/null +++ b/spatialvid_eval/source/scene_138/caption.txt @@ -0,0 +1 @@ +At night along a bustling riverside in Seoul, people gather around a white-clothed table displaying various items, engaging with vendors and each other. A tall banner with Korean text and a QR code stands nearby, while the background reveals a lively crowd seated and standing, enjoying the illuminated waterfront and city skyline. The scene captures a vibrant, social atmosphere with individuals browsing, chatting, and exploring the offerings under the glow of city lights. diff --git a/spatialvid_eval/source/scene_138/extras/temporal_mapping.json b/spatialvid_eval/source/scene_138/extras/temporal_mapping.json new file mode 100644 index 0000000000000000000000000000000000000000..8e72504282feeed3e8574d706e67e2c13aa6e626 --- /dev/null +++ b/spatialvid_eval/source/scene_138/extras/temporal_mapping.json @@ -0,0 +1,182 @@ +{ + "original_scene_id": "f908eea8-16f6-5613-b0ca-a3dcf33bdd90", + "original_video_path": "videos/group_0044/f908eea8-16f6-5613-b0ca-a3dcf33bdd90.mp4", + "original_video_num_frames": 397, + "original_annotation_num_frames": 36, + "source_frame_indices": [ + 0, + 5, + 10, + 14, + 19, + 24, + 29, + 34, + 38, + 43, + 48, + 53, + 58, + 63, + 67, + 72, + 77, + 82, + 87, + 91, + 96, + 101, + 106, + 111, + 116, + 120, + 125, + 130, + 135, + 140, + 144, + 149, + 154, + 159, + 164, + 168, + 173, + 178, + 183, 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new file mode 100644 index 0000000000000000000000000000000000000000..c7f8e50859309e36e7bf21d0aeea98dae3f059d6 --- /dev/null +++ b/spatialvid_eval/source/scene_147/caption.txt @@ -0,0 +1 @@ +A group of people stroll along a vibrant elevated park, flanked by urban buildings and lush greenery, with some pausing to sit and enjoy the sunny day. diff --git a/spatialvid_eval/source/scene_147/extras/temporal_mapping.json b/spatialvid_eval/source/scene_147/extras/temporal_mapping.json new file mode 100644 index 0000000000000000000000000000000000000000..38ba34ce3c7411a565e7adae458093c521e5633c --- /dev/null +++ b/spatialvid_eval/source/scene_147/extras/temporal_mapping.json @@ -0,0 +1,182 @@ +{ + "original_scene_id": "e89ca97b-dac7-5c44-ba68-568b8df7e228", + "original_video_path": "videos/group_0028/e89ca97b-dac7-5c44-ba68-568b8df7e228.mp4", + "original_video_num_frames": 501, + "original_annotation_num_frames": 46, + "source_frame_indices": [ + 0, + 6, + 12, + 19, + 25, + 31, + 37, + 43, + 50, 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by lush hills and clear skies. diff --git a/spatialvid_eval/source/scene_157/extras/temporal_mapping.json b/spatialvid_eval/source/scene_157/extras/temporal_mapping.json new file mode 100644 index 0000000000000000000000000000000000000000..b4878626b4a7f5059b5b7788f91399148a1dcd06 --- /dev/null +++ b/spatialvid_eval/source/scene_157/extras/temporal_mapping.json @@ -0,0 +1,182 @@ +{ + "original_scene_id": "70c68287-b259-5dda-ae92-8b75c9a54cfc", + "original_video_path": "videos/group_0031/70c68287-b259-5dda-ae92-8b75c9a54cfc.mp4", + "original_video_num_frames": 360, + "original_annotation_num_frames": 90, + "source_frame_indices": [ + 0, + 4, + 9, + 13, + 18, + 22, + 27, + 31, + 36, + 40, + 44, + 49, + 53, + 58, + 62, + 67, + 71, + 76, + 80, + 85, + 89, + 93, + 98, + 102, + 107, + 111, + 116, + 120, + 125, + 129, + 134, + 138, + 142, + 147, + 151, + 156, + 160, + 165, + 169, + 174, + 178, + 182, + 187, + 191, + 196, + 200, + 205, + 209, + 214, + 218, + 222, + 227, + 231, + 236, + 240, + 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predefined boom_down control for SpatialVID", + "mode": "relative", + "num_frames": 81, + "zoom_ratio": 1.0, + "use_first_frame": true, + "composition": "target_c2w[t] = source_c2w[t] @ relative_c2w[t]", + "keyframes": [ + { + "0": [ + { + "static": {} + } + ] + }, + { + "80": [ + { + "boom_down": { + "distance": 0.1 + } + } + ] + } + ] +} diff --git a/spatialvid_eval/trajectory_specs/boom_up.json b/spatialvid_eval/trajectory_specs/boom_up.json new file mode 100644 index 0000000000000000000000000000000000000000..6f0e1bbac25a3bfcc156af27964fa051639de5e5 --- /dev/null +++ b/spatialvid_eval/trajectory_specs/boom_up.json @@ -0,0 +1,27 @@ +{ + "name": "boom_up", + "description": "NeoVerse predefined boom_up control for SpatialVID", + "mode": "relative", + "num_frames": 81, + "zoom_ratio": 1.0, + "use_first_frame": true, + "composition": "target_c2w[t] = source_c2w[t] @ relative_c2w[t]", + "keyframes": [ + { + "0": [ + { + "static": {} + } + ] + }, + { + "80": [ + { + "boom_up": { + "distance": 0.1 + } + } + ] + } + ] +} diff --git a/spatialvid_eval/trajectory_specs/move_left.json b/spatialvid_eval/trajectory_specs/move_left.json new file mode 100644 index 0000000000000000000000000000000000000000..2c0159c481706c98c8ba83d813b68ef71cef74ec --- /dev/null +++ b/spatialvid_eval/trajectory_specs/move_left.json @@ -0,0 +1,27 @@ +{ + "name": "move_left", + "description": "NeoVerse predefined move_left control for SpatialVID", + "mode": "relative", + "num_frames": 81, + "zoom_ratio": 1.0, + "use_first_frame": true, + "composition": "target_c2w[t] = source_c2w[t] @ relative_c2w[t]", + "keyframes": [ + { + "0": [ + { + "static": {} + } + ] + }, + { + "80": [ + { + "move_left": { + "distance": 0.1 + } + } + ] + } + ] +} diff --git a/spatialvid_eval/trajectory_specs/move_right.json b/spatialvid_eval/trajectory_specs/move_right.json new file mode 100644 index 0000000000000000000000000000000000000000..14b1f11a067f8663342b013b2e57a532bdf59c8a --- /dev/null +++ b/spatialvid_eval/trajectory_specs/move_right.json @@ -0,0 +1,27 @@ +{ + "name": "move_right", + "description": "NeoVerse predefined move_right control for SpatialVID", + "mode": "relative", + "num_frames": 81, + "zoom_ratio": 1.0, + "use_first_frame": true, + "composition": "target_c2w[t] = source_c2w[t] @ relative_c2w[t]", + "keyframes": [ + { + "0": [ + { + "static": {} + } + ] + }, + { + "80": [ + { + "move_right": { + "distance": 0.1 + } + } + ] + } + ] +} diff --git a/spatialvid_eval/trajectory_specs/orbit_left.json b/spatialvid_eval/trajectory_specs/orbit_left.json new file mode 100644 index 0000000000000000000000000000000000000000..11f72873d0474c278bc0c36306ce2e470bd44f9d --- /dev/null +++ b/spatialvid_eval/trajectory_specs/orbit_left.json @@ -0,0 +1,28 @@ +{ + "name": "orbit_left", + "description": "NeoVerse predefined orbit_left control for SpatialVID", + "mode": "relative", + "num_frames": 81, + "zoom_ratio": 1.0, + "use_first_frame": true, + "composition": "target_c2w[t] = source_c2w[t] @ relative_c2w[t]", + "keyframes": [ + { + "0": [ + { + "static": {} + } + ] + }, + { + "80": [ + { + "orbit_left": { + "angle": 15, + "orbit_radius": 1.0 + } + } + ] + } + ] +} diff --git a/spatialvid_eval/trajectory_specs/orbit_right.json b/spatialvid_eval/trajectory_specs/orbit_right.json new file mode 100644 index 0000000000000000000000000000000000000000..3b686597513c1441318284c98e1849ff65087fc6 --- /dev/null +++ b/spatialvid_eval/trajectory_specs/orbit_right.json @@ -0,0 +1,28 @@ +{ + "name": "orbit_right", + "description": "NeoVerse predefined orbit_right control for SpatialVID", + "mode": "relative", + "num_frames": 81, + "zoom_ratio": 1.0, + "use_first_frame": true, + "composition": "target_c2w[t] = source_c2w[t] @ relative_c2w[t]", + "keyframes": [ + { + "0": [ + { + "static": {} + } + ] + }, + { + "80": [ + { + "orbit_right": { + "angle": 15, + "orbit_radius": 1.0 + } + } + ] + } + ] +} diff --git a/spatialvid_eval/trajectory_specs/pan_left.json b/spatialvid_eval/trajectory_specs/pan_left.json new file mode 100644 index 0000000000000000000000000000000000000000..a2f540bf232251e8b22f26687610f5cf092a5940 --- /dev/null +++ b/spatialvid_eval/trajectory_specs/pan_left.json @@ -0,0 +1,27 @@ +{ + "name": "pan_left", + "description": "NeoVerse predefined pan_left control for SpatialVID", + "mode": "relative", + "num_frames": 81, + "zoom_ratio": 1.0, + "use_first_frame": true, + "composition": "target_c2w[t] = source_c2w[t] @ relative_c2w[t]", + "keyframes": [ + { + "0": [ + { + "static": {} + } + ] + }, + { + "80": [ + { + "pan_left": { + "angle": 15 + } + } + ] + } + ] +} diff --git a/spatialvid_eval/trajectory_specs/pan_right.json b/spatialvid_eval/trajectory_specs/pan_right.json new file mode 100644 index 0000000000000000000000000000000000000000..760cc283fe6c70b821fcf82007ed90cfaaf65f90 --- /dev/null +++ b/spatialvid_eval/trajectory_specs/pan_right.json @@ -0,0 +1,27 @@ +{ + "name": "pan_right", + "description": "NeoVerse predefined pan_right control for SpatialVID", + "mode": "relative", + "num_frames": 81, + "zoom_ratio": 1.0, + "use_first_frame": true, + "composition": "target_c2w[t] = source_c2w[t] @ relative_c2w[t]", + "keyframes": [ + { + "0": [ + { + "static": {} + } + ] + }, + { + "80": [ + { + "pan_right": { + "angle": 15 + } + } + ] + } + ] +} diff --git a/spatialvid_eval/trajectory_specs/pull_out.json b/spatialvid_eval/trajectory_specs/pull_out.json new file mode 100644 index 0000000000000000000000000000000000000000..8fd1f52b4fa1bdcc12e3fbd3b31cb67ec9134691 --- /dev/null +++ b/spatialvid_eval/trajectory_specs/pull_out.json @@ -0,0 +1,27 @@ +{ + "name": "pull_out", + "description": "NeoVerse predefined pull_out control for SpatialVID", + "mode": "relative", + "num_frames": 81, + "zoom_ratio": 1.0, + "use_first_frame": true, + "composition": "target_c2w[t] = source_c2w[t] @ relative_c2w[t]", + "keyframes": [ + { + "0": [ + { + "static": {} + } + ] + }, + { + "80": [ + { + "pull_out": { + "distance": 0.1 + } + } + ] + } + ] +} diff --git a/spatialvid_eval/trajectory_specs/push_in.json b/spatialvid_eval/trajectory_specs/push_in.json new file mode 100644 index 0000000000000000000000000000000000000000..a3f441ecfc07230bd8ffe4963fd73bed2250ddd4 --- /dev/null +++ b/spatialvid_eval/trajectory_specs/push_in.json @@ -0,0 +1,27 @@ +{ + "name": "push_in", + "description": "NeoVerse predefined push_in control for SpatialVID", + "mode": "relative", + "num_frames": 81, + "zoom_ratio": 1.0, + "use_first_frame": true, + "composition": "target_c2w[t] = source_c2w[t] @ relative_c2w[t]", + "keyframes": [ + { + "0": [ + { + "static": {} + } + ] + }, + { + "80": [ + { + "push_in": { + "distance": 0.1 + } + } + ] + } + ] +} diff --git a/spatialvid_eval/trajectory_specs/tilt_down.json b/spatialvid_eval/trajectory_specs/tilt_down.json new file mode 100644 index 0000000000000000000000000000000000000000..02d745a0234e70e0a63c26834b0eac6c06237885 --- /dev/null +++ b/spatialvid_eval/trajectory_specs/tilt_down.json @@ -0,0 +1,27 @@ +{ + "name": "tilt_down", + "description": "NeoVerse predefined tilt_down control for SpatialVID", + "mode": "relative", + "num_frames": 81, + "zoom_ratio": 1.0, + "use_first_frame": true, + "composition": "target_c2w[t] = source_c2w[t] @ relative_c2w[t]", + "keyframes": [ + { + "0": [ + { + "static": {} + } + ] + }, + { + "80": [ + { + "tilt_down": { + "angle": 15 + } + } + ] + } + ] +} diff --git a/spatialvid_eval/trajectory_specs/tilt_up.json b/spatialvid_eval/trajectory_specs/tilt_up.json new file mode 100644 index 0000000000000000000000000000000000000000..b07c5f3a7d99f3c5c6b32bfffc3e7787bf968680 --- /dev/null +++ b/spatialvid_eval/trajectory_specs/tilt_up.json @@ -0,0 +1,27 @@ +{ + "name": "tilt_up", + "description": "NeoVerse predefined tilt_up control for SpatialVID", + "mode": "relative", + "num_frames": 81, + "zoom_ratio": 1.0, + "use_first_frame": true, + "composition": "target_c2w[t] = source_c2w[t] @ relative_c2w[t]", + "keyframes": [ + { + "0": [ + { + "static": {} + } + ] + }, + { + "80": [ + { + "tilt_up": { + "angle": 15 + } + } + ] + } + ] +}