Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video (2011.08627v4)

Abstract: Despite the recent success of single image-based 3D human pose and shape estimation methods, recovering temporally consistent and smooth 3D human motion from a video is still challenging. Several video-based methods have been proposed; however, they fail to resolve the single image-based methods' temporal inconsistency issue due to a strong dependency on a static feature of the current frame. In this regard, we present a temporally consistent mesh recovery system (TCMR). It effectively focuses on the past and future frames' temporal information without being dominated by the current static feature. Our TCMR significantly outperforms previous video-based methods in temporal consistency with better per-frame 3D pose and shape accuracy. We also release the codes. For the demo video, see https://youtu.be/WB3nTnSQDII. For the codes, see https://github.com/hongsukchoi/TCMR_RELEASE.

• The paper demonstrates that eliminating residual static connections enables deeper temporal learning for smoother 3D human pose estimation.
• TCMR introduces PoseForecasting to predict current poses from past and future frames, reducing reliance on static frame details.
• Empirical results on benchmarks like 3DPW and Human3.6M show reduced acceleration errors and improved accuracy compared to prior methods.

Temporally Consistent 3D Human Pose and Shape Estimation from Video

The paper "Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video" addresses a prominent challenge in video-based human pose estimation: achieving temporal consistency and smoothness in 3D motion reconstruction. The authors introduce a novel system termed Temporally Consistent Mesh Recovery (TCMR) that effectively reduces the dependency on static features of individual video frames, thereby enhancing the temporal consistency and smoothness of the generated 3D human poses.

Overview of Prior Work

Previous solutions primarily focused on extending single-image based methods to video processing, which often resulted in temporally inconsistent outputs. Methods such as VIBE faced limitations due to a strong reliance on static features, leading to artifacts in consecutive frames. Although techniques like residual connections facilitate learning, they inadvertently constrained temporal encoding by emphasizing static information from the current frame.

Proposed Method: TCMR

TCMR proposes to resolve these challenges by modifying the typical architecture of video-based 3D human pose estimators in two key ways:

1. Removal of Residual Connections: By eliminating the residual connection between static and temporal features, TCMR mitigates the dominance of the current static feature, encouraging the system to learn more meaningful temporal features.
2. Introduction of PoseForecast: This component forecasts the current frame's pose utilizing temporal information from the past and future frames without relying on the current frame itself. By doing so, it further reinforces the temporal aspect of the data, allowing the system to better exploit temporal cues for improved consistency and smoothness.

The architecture also includes a process for temporal feature integration that aligns and weights the contributions from past, future, and current temporal features. This ensures that the most relevant temporal data is used in final pose estimation.

Empirical Validation and Results

The authors provide robust empirical evidence of TCMR's effectiveness by evaluating their model on multiple standard benchmarks such as 3DPW, MPI-INF-3DHP, and Human3.6M. The results indicate that TCMR not only achieves superior temporal consistency, as evidenced by reduced acceleration errors across datasets, but also improves per-frame pose accuracy over existing video-based methods such as MEVA and VIBE.

A notable highlight from the results is TCMR's performance in maintaining smooth and temporally coherent 3D motion, even when compared with methods applied with post-processing techniques like average filtering. This underscores the model's ability to naturally integrate temporal information without over-reliance on frame-level details.

Implications and Future Directions

TCMR sets a strong precedent for incorporating temporal information in video-based human pose estimation, effectively balancing per-frame accuracy with temporal coherence—a dual challenge in the domain. The system’s architecture implies future research could explore even more refined temporal forecasting techniques, perhaps incorporating predictions of scene dynamics or inter-person interactions to further enhance realism.

More broadly, the implications of this research extend to any domain where smooth human motion capture from video is necessary, such as animation, sports analysis, and augmented reality applications. Future developments could focus on integrating additional sensory data (e.g., IMU, depth information) to improve accuracy, especially in challenging environmental conditions or with occluded subjects.

In summary, the TCMR framework offers a significant advancement in the video-based 3D human pose estimation by enhancing temporal consistency through innovative architectural choices that prioritize temporal over static information. This research opens new avenues for advancements in human pose estimation and related applications.