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Amodal3R_ckpts/slat_dec_gs_swin8_B_64l8gs32_fp16.json

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+{
+    "name": "SLatGaussianDecoder",
+    "args": {
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+        "model_channels": 768,
+        "latent_channels": 8,
+        "num_blocks": 12,
+        "num_heads": 12,
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+            "num_gaussians": 32,
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+            "opacity_bias": 0.1,
+            "scaling_activation": "softplus"
+        }
+    }
+}

Amodal3R_ckpts/slat_dec_gs_swin8_B_64l8gs32_fp16.safetensors

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Amodal3R_ckpts/slat_dec_mesh_swin8_B_64l8m256c_fp16.json

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Amodal3R_ckpts/slat_flow_img_dit_L_64l8p2_fp16_doubleattn_weighted.json

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Amodal3R_ckpts/slat_flow_img_dit_L_64l8p2_fp16_doubleattn_weighted.safetensors

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Amodal3R_ckpts/ss_dec_conv3d_16l8_fp16.json

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+{
+    "name": "SparseStructureDecoder",
+    "args": {
+        "out_channels": 1,
+        "latent_channels": 8,
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Amodal3R_ckpts/ss_flow_img_dit_L_16l8_fp16_doubleattn_weighted.json

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+    "name": "SparseStructureFlowModelMaskAsCondWeighted",
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README.md

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 ---
 license: cc-by-4.0
 ---
+
+# **Amodal3R**: Amodal 3D Reconstruction from Occluded 2D Images
+
+Given partially visible objects within images, Amodal3R reconstructs semantically meaningful 3D assets with reasonable geometry and plausible appearance.
+
+Arxiv:
+
+Project Page: https://sm0kywu.github.io/Amodal3R/
+
+Our model is built upon the "foundation" model [TRELLIS](https://trellis3d.github.io/). The pre-trained model weights are fetched from https://huggingface.co/JeffreyXiang/TRELLIS-image-large. Thanks to their impressive work!!!

pipeline.json

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+{
+    "name": "Amodal3RImageTo3DPipeline",
+    "args": {
+        "models": {
+            "sparse_structure_decoder": "Amodal3R_ckpts/ss_dec_conv3d_16l8_fp16",
+            "sparse_structure_flow_model": "Amodal3R_ckpts/ss_flow_img_dit_L_16l8_fp16_doubleattn_weighted",
+            "slat_decoder_gs": "Amodal3R_ckpts/slat_dec_gs_swin8_B_64l8gs32_fp16",
+            "slat_decoder_mesh": "Amodal3R_ckpts/slat_dec_mesh_swin8_B_64l8m256c_fp16",
+            "slat_flow_model": "Amodal3R_ckpts/slat_flow_img_dit_L_64l8p2_fp16_doubleattn_weighted"
+        },
+        "sparse_structure_sampler": {
+            "name": "FlowEulerGuidanceIntervalSampler",
+            "args": {
+                "sigma_min": 1e-5
+            },
+            "params": {
+                "steps": 12,
+                "cfg_strength": 7.5,
+                "cfg_interval": [0.5, 1.0],
+                "rescale_t": 3.0
+            }
+        },
+        "slat_sampler": {
+            "name": "FlowEulerGuidanceIntervalSampler",
+            "args": {
+                "sigma_min": 1e-5
+            },
+            "params": {
+                "steps": 12,
+                "cfg_strength": 3.0,
+                "cfg_interval": [0.5, 1.0],
+                "rescale_t": 3.0
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+            "mean": [
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+                -0.004347046371549368,
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+                1.2039363384246826
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+            "std": [
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+                2.124418020248413,
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+                2.6217446327209473,
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+            ]
+        },
+        "image_cond_model": "dinov2_vitl14_reg"
+    }
+}