Research map / 3D vision / Human pose estimation
HandDiff: 3D Hand Pose Estimation with Diffusion on Image-Point Cloud
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Proposes HandDiff, a diffusion-based 3D hand pose estimator that iteratively denoises joint coordinates conditioned on joint-wise, local 2D/3D features from depth images and point clouds.
Problem
3D hand pose estimation from depth images/point clouds, where deterministic regressors struggle with occlusion uncertainty and existing 3D diffusion models lack joint permutation awareness and local detail.
Approach
Applies a DDIM-accelerated diffusion process directly over 3D joint coordinates, with joint-wise condition embeddings, K-nearest-neighbor local feature sampling from both 2D depth and 3D point features, and a graph-based kinematic correspondence aggregation block; single-stage, no 2D pose prior.
Key results
- Achieves 5.72 mm on ICVL and 6.53 mm on MSRA, new state of the art on those datasets (IPNet 5.76 and 6.92 mm).
- On DexYCB, average error 8.07 mm vs. IPNet 8.36 mm across S0-S3 protocols.
- On NYU, 7.38 mm error, third-lowest among compared methods (IPNet 7.17 mm).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use HandDiff instead of IPNet | 3D hand pose estimation · ICVL | mean joint error (mm): 5.76 -> 5.72 | Helped |
| Use HandDiff instead of IPNet | 3D hand pose estimation · MSRA | mean joint error (mm): 6.92 -> 6.53 | Helped |
| Use HandDiff instead of IPNet | 3D hand pose estimation · NYU | mean joint error (mm): 7.17 -> 7.38 | Hurt |
| Use HandDiff instead of IPNet | 3D hand pose estimation · DexYCB S0, S1, S2, and S3 | mean joint error (mm): 8.03 -> 7.66; 9.01 -> 8.73; 8.60 -> 8.40; 7.80 -> 7.53 | Helped |
| Add joint-wise condition, local condition, joint indicator, kinematic correspondence, and multiple hypotheses (ablation) | HandDiff on DexYCB · DexYCB | mean joint error: 9.17 mm -> 7.66 mm | Helped |
| Use local condition alone without joint-order information (ablation) | 3DDPM on DexYCB · DexYCB | mean joint error: 49.58 mm | Hurt |
| Add joint indicator and joint-wise condition to local-condition denoising (ablation) | 3DDPM on DexYCB · DexYCB | mean joint error: 49.58 mm -> 8.13 mm | Helped |
| Add local condition with joint indicator (ablation) | HandDiff on DexYCB · DexYCB | mean joint error: 8.37 mm -> 8.23 mm | Helped |
| Add local condition to joint-wise condition (ablation) | HandDiff on DexYCB · DexYCB | mean joint error: 9.17 mm -> 8.28 mm | Helped |
| Add kinematic correspondence-aware aggregation (ablation) | HandDiff on DexYCB · DexYCB | mean joint error: 8.13 mm -> 7.94 mm | Helped |
| Add multiple hypotheses (ablation) | HandDiff on DexYCB · DexYCB | mean joint error: 7.74 mm -> 7.66 mm | Helped |
| Combine 2D depth and 3D point conditions instead of using either alone (ablation) | HandDiff · DexYCB | mean joint error: 8.23 -> 7.74; 9.58 -> 7.74 | Helped |
Surprising findings
- Local conditions alone are permutation-equivariant and cannot work without joint indicator or joint-wise conditions, yielding 49.58 mm error when used alone.
Limitations
- Cannot handle interacting hands scenarios.
- Only evaluated with single-frame depth/point inputs from the stated benchmarks.
Experimental setup
Datasets: ICVL MSRA NYU DexYCB
Benchmarks: ICVL MSRA NYU DexYCB
Metrics: mean joint error success rate
Baselines: IPNet HandFoldingNet Point-to-Point PHG VVS JGR-P2O SSRN A2J
Builds on: DDPM DDIM 3DDPM HandFoldingNet PointNet++
Closest papers by meaning
- HOIDiffusion: Generating Realistic 3D Hand-Object Interaction Data (CVPR 2024 · similarity 0.88)
- HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields (CVPR 2024 · similarity 0.87)
- The Surprising Effectiveness of Video Diffusion Models for Hand Motion Reconstruction (NeurIPS 2026 · similarity 0.87)
- Single-view Image to Novel-view Generation for Hand-Object Interactions (AAAI 2025 · similarity 0.86)
- Keypoint Fusion for RGB-D Based 3D Hand Pose Estimation (AAAI 2024 · similarity 0.86)
- 6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation (CVPR 2024 · similarity 0.86)
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