We lifted 8 functions out of this paper's own repositories and ran 0 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 |
|---|---|---|
| Jeff-sjtu/HybrIK | canonical | 0 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| flip | Not yet run | Jeff-sjtu/HybrIK/hybrik/models/HRNetSMPLXCamKid.py code served (permissive licence) · get_code("fabada0c3f6b8840") |
| focalLength_mm2px | Not yet run | Jeff-sjtu/HybrIK/hybrik/datasets/agora_smplx.py code served (permissive licence) · get_code("ed832a8183efcc97") |
| get_focal | Not yet run | Jeff-sjtu/HybrIK/hybrik/datasets/agora_smplx.py code served (permissive licence) · get_code("733d8811b481fdbe") |
| norm_heatmap | Not yet run | Jeff-sjtu/HybrIK/hybrik/models/HRNetSMPLXCamKid.py code served (permissive licence) · get_code("0635936243f338d5") |
| norm_heatmap | Not yet run | Jeff-sjtu/HybrIK/hybrik/models/simple3dposeBaseSMPL.py code served (permissive licence) · get_code("a828d7ddc0f76aac") |
| project | Not yet run | Jeff-sjtu/HybrIK/hybrik/datasets/agora_smplx.py code served (permissive licence) · get_code("8cc86609ecbd6234") |
| weighted_l1_loss | Not yet run | Jeff-sjtu/HybrIK/hybrik/models/criterion.py code served (permissive licence) · get_code("3b94245b39283e5c") |
| weighted_laplace_loss | Not yet run | Jeff-sjtu/HybrIK/hybrik/models/criterion.py code served (permissive licence) · get_code("86dec2a5c79f02f6") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recovering whole-body mesh by inferring the abstract pose and shape parameters from visual content can obtain 3D bodies with realistic structures. However, the inferring process is highly non-linear and suffers from image-mesh misalignment, resulting in inaccurate reconstruction. In contrast, 3D keypoint estimation methods utilize the volumetric representation to achieve pixel-level accuracy but may predict unrealistic body structures. To address these issues, this paper presents a novel hybrid inverse kinematics solution, HybrIK, that integrates the merits of 3D keypoint estimation and body mesh recovery in a unified framework. HybrIK directly transforms accurate 3D joints to body-part rotations via twist-and-swing decomposition. The swing rotations are analytically solved with 3D joints, while the twist rotations are derived from visual cues through neural networks. To capture comprehensive whole-body details, we further develop a holistic framework, HybrIK-X, which enhances HybrIK with articulated hands and an expressive face. HybrIK-X is fast and accurate by solving the whole-body pose with a one-stage model. Experiments demonstrate that HybrIK and HybrIK-X preserve both the accuracy of 3D joints and the realistic structure of the parametric human model, leading to pixel-aligned whole-body mesh recovery. The proposed method significantly surpasses the state-of-the-art methods on various benchmarks for body-only, hand-only, and whole-body scenarios. Code and results can be found at https://jeffli.site/HybrIK-X/
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2304.05690")
get_code_for_paper("2304.05690")
have("2304.05690")
Connect an agent — have() is free.