Jan Kautz, Ye Yuan, Davis Rempe, Xue Peng, Sanja Fidler, Haotian Zhang, Jiefeng Li, Yifeng Jiang, Chen Tessler, Michael Huang, Chaeyeon Chung, Umar Iqbal, and 12 more
We lifted 17 functions out of this paper's own repositories and ran 12 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| nv-tlabs/kimodo | canonical | 12 of 17 |
| Function | Status | Where it lives |
|---|---|---|
| angle_to_Y_rotation_matrix | Ran | nv-tlabs/kimodo/kimodo/geometry.py code served (permissive licence) · get_code("d6b4add804976434") |
| cont6d_to_matrix | Ran | nv-tlabs/kimodo/kimodo/geometry.py code served (permissive licence) · get_code("42c47c90157ca5e4") |
| create_pairs | Ran | nv-tlabs/kimodo/kimodo/constraints.py code served (permissive licence) · get_code("495275fac958edb6") |
| get_beta_schedule | Ran | nv-tlabs/kimodo/kimodo/model/diffusion.py code served (permissive licence) · get_code("0c0a342a6d097b28") |
| get_env_var | Ran | nv-tlabs/kimodo/kimodo/model/common.py code served (permissive licence) · get_code("1261e7b3e8e04b68") |
| get_skeleton_display_name | Ran | nv-tlabs/kimodo/kimodo/model/registry.py code served (permissive licence) · get_code("a5998a861ba6879c") |
| get_skeleton_key_from_display_name | Ran | nv-tlabs/kimodo/kimodo/model/registry.py code served (permissive licence) · get_code("ca4f17050d7f391c") |
| load_checkpoint_state_dict | Ran | nv-tlabs/kimodo/kimodo/model/loading.py code served (permissive licence) · get_code("e95c6635f9e67e38") |
| matrix_to_cont6d | Ran | nv-tlabs/kimodo/kimodo/geometry.py code served (permissive licence) · get_code("0ae30a547c08a556") |
| pad_x_and_mask_to_fixed_size | Ran | nv-tlabs/kimodo/kimodo/model/backbone.py code served (permissive licence) · get_code("2609d3cb0326a521") |
| parse_prompts_from_meta | Ran | nv-tlabs/kimodo/kimodo/meta.py code served (permissive licence) · get_code("a06db7b4a4f76637") |
| resolve_target | Ran | nv-tlabs/kimodo/kimodo/model/common.py code served (permissive licence) · get_code("debe7d25330cd587") |
| get_env_var | Not yet run | nv-tlabs/kimodo/kimodo/model/loading.py code served (permissive licence) · get_code("a9ce08fb03d39b34") |
| get_skeleton_display_names_for_dataset | Not yet run | nv-tlabs/kimodo/kimodo/model/registry.py code served (permissive licence) · get_code("0ef3379f93d586a4") |
| instantiate_from_dict | Not yet run | nv-tlabs/kimodo/kimodo/model/loading.py code served (permissive licence) · get_code("006930e776ca886b") |
| load_model | Not yet run | nv-tlabs/kimodo/kimodo/model/load_model.py code served (permissive licence) · get_code("9864a44c22701ca7") |
| materialize_value | Not yet run | nv-tlabs/kimodo/kimodo/model/common.py code served (permissive licence) · get_code("d4eeec6d214fdc47") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
High-quality human motion data is becoming increasingly important for applications in robotics, simulation, and entertainment. Recent generative models offer a potential data source, enabling human motion synthesis through intuitive inputs like text prompts or kinematic constraints on poses. However, the small scale of public mocap datasets has limited the motion quality, control accuracy, and generalization of these models. In this work, we introduce Kimodo, an expressive and controllable kinematic motion diffusion model trained on 700 hours of optical motion capture data. Our model generates high-quality motions while being easily controlled through text and a comprehensive suite of kinematic constraints including full-body keyframes, sparse joint positions/rotations, 2D waypoints, and dense 2D paths. This is enabled through a carefully designed motion representation and two-stage denoiser architecture that decomposes root and body prediction to minimize motion artifacts while allowing for flexible constraint conditioning. Experiments on the large-scale mocap dataset justify key design decisions and analyze how the scaling of dataset size and model size affect performance.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2603.15546")
get_code_for_paper("2603.15546")
have("2603.15546")
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