We lifted 3 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| snuvclab/coma | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| apply_postfilter | Ran | snuvclab/coma/src/coma/extract_coma.py pointer only (licence: NOASSERTION) · get_code("f7f03120e38a60f5") |
| geodesic_gaussian_scores | Ran | snuvclab/coma/utils/coma.py pointer only (licence: NOASSERTION) · get_code("181c26bba18b17c0") |
| get_uniform_points_on_sphere | Ran | snuvclab/coma/utils/coma.py pointer only (licence: NOASSERTION) · get_code("69ad2a1d167ef4ae") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Understanding the inherent human knowledge in interacting with a given environment (e.g., affordance) is essential for improving AI to better assist humans. While existing approaches primarily focus on human-object contacts during interactions, such affordance representation cannot fully address other important aspects of human-object interactions (HOIs), i.e., patterns of relative positions and orientations. In this paper, we introduce a novel affordance representation, named Comprehensive Affordance (ComA). Given a 3D object mesh, ComA models the distribution of relative orientation and proximity of vertices in interacting human meshes, capturing plausible patterns of contact, relative orientations, and spatial relationships. To construct the distribution, we present a novel pipeline that synthesizes diverse and realistic 3D HOI samples given any 3D object mesh. The pipeline leverages a pre-trained 2D inpainting diffusion model to generate HOI images from object renderings and lifts them into 3D. To avoid the generation of false affordances, we propose a new inpainting framework, Adaptive Mask Inpainting. Since ComA is built on synthetic samples, it can extend to any object in an unbounded manner. Through extensive experiments, we demonstrate that ComA outperforms competitors that rely on human annotations in modeling contact-based affordance. Importantly, we also showcase the potential of ComA to reconstruct human-object interactions in 3D through an optimization framework, highlighting its advantage in incorporating both contact and non-contact properties.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2401.12978")
get_code_for_paper("2401.12978")
have("2401.12978")
Connect an agent — have() is free.