Research map / 3D vision / Human pose estimation
SynSP: Synergy of Smoothness and Precision in Pose Sequences Refinement
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Proposes SynSP, a transformer-based motion refinement network that uses the discrepancy between smoothness-biased and precision-biased branches as a pose quality cue, plus pose similarity encoding across views, to refine pose sequences with a short window (T=8).
Problem
Pose sequences from image-based or video-based pose estimators contain jitter and position errors; motion refinement must improve both precision (MPJPE) and smoothness (acceleration error) with low latency, and the trade-off between these objectives is typically handled by balancing them.
Approach
Treats the tension between smoothness and precision as a signal: the disagreement between a precision-biased and a smoothness-biased branch serves as a pose quality encoding (PQE) that reweights attention, and cross-view pose similarity encoding (PSE) lets multi-view poses correct each other; a refinement decoder reuses embeddings instead of re-running the base pipeline.
Key results
- On Human3.6M with FCN 3D estimator, SynSP improves MPJPE by 20.8% over SmoothNet (51.36 vs 52.72 for 2D; 41.78 multi-view) while using window 8 instead of 32.
- On CMU-Mocap denoising with N(0,100) noise, SynSP reaches MPJPE 20.6 vs 35.1 for SmoothNet and 40.4 for GFPose.
- Ablation on Human3.6M shows the full model reaches MPJPE 41.8 and Accel 0.98 versus 51.2 and 1.09 for the base configuration without the added modules.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace SmoothNet with SynSP refinement | Hourglass pose sequences · Human3.6M / 2D | MPJPE: 9.25 -> 8.13 | Helped |
| replace SmoothNet with SynSP refinement | FCN pose sequences · Human3.6M / 3D | MPJPE: 52.72 -> 51.36 | Helped |
| replace SmoothNet with SynSP refinement | SPIN pose sequences · AIST++ / SMPL | MPJPE: 103.00 -> 84.63 | Helped |
| replace SmoothNet with SynSP refinement | VIBE pose sequences · AIST++ | MPJPE: 97.47 -> 77.00 | Helped |
| replace SmoothNet with SynSP refinement | PARE pose sequences · 3DPW | Accel: 5.91 -> 6.16 | Hurt |
| replace SmoothNet with SynSP refinement | PPT pose sequences · Human3.6M | MPJPE: 23.97 -> 21.51 | Helped |
| add PSE I (ablation) | SynSP · Human3.6M / 3D | MPJPE: 51.2 -> 48.7 | Helped |
| add the refinement stage using averaged branch outputs (ablation) | SynSP · Human3.6M / 3D | MPJPE: 51.2 -> 51.4 | Hurt |
| add the complete proposed modules, including PSE I, refinement stage, PSE II, and PQE (ablation) | SynSP · Human3.6M / 3D | MPJPE: 51.2 -> 41.8 | Helped |
Surprising findings
- Simply averaging the two branch outputs and re-running the base pipeline did not help and doubled compute.
- Video-based estimator TCMR was hard to refine further, with SmoothNet barely improving its MPJPE.
Limitations
- Multi-view refinement evaluated mainly on Human3.6M; multi-view results not available for other datasets
- Pose quality cue is an empirical correlation (Pearson about 0.48) rather than a verified causal signal
- Window-based post-processing still adds latency tied to the window size
Experimental setup
Datasets: Human3.6M AIST++ 3DPW CMU-Mocap
Benchmarks: Human3.6M AIST++ 3DPW CMU-Mocap
Metrics: MPJPE PA-MPJPE Accel error
Baselines: SmoothNet One-Euro Gaussian1d Savitzky-Golay GFPose TCMR VIBE PARE PPT TransFusion
Builds on: SmoothNet Transformer
Closest papers by meaning
- The Unreasonable Effectiveness of Pre-Trained Features for Camera Pose Refinement (CVPR 2024 · similarity 0.85)
- Can Generative Video Models Help Pose Estimation? (CVPR 2025 · similarity 0.85)
- MotionMap: Representing Multimodality in Human Pose Forecasting (CVPR 2025 · similarity 0.84)
- Video-Based Human Pose Regression via Decoupled Space-Time Aggregation (CVPR 2024 · similarity 0.84)
- HumanVid: Demystifying Training Data for Camera-controllable Human Image Animation (NeurIPS 2024 · similarity 0.84)
- CHAMP: Conformalized 3D Human Multi-Hypothesis Pose Estimators (ICLR 2025 · similarity 0.84)
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