Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh Reconstruction (2308.06554v1)

Abstract: Despite recent advances in 3D human mesh reconstruction, domain gap between training and test data is still a major challenge. Several prior works tackle the domain gap problem via test-time adaptation that fine-tunes a network relying on 2D evidence (e.g., 2D human keypoints) from test images. However, the high reliance on 2D evidence during adaptation causes two major issues. First, 2D evidence induces depth ambiguity, preventing the learning of accurate 3D human geometry. Second, 2D evidence is noisy or partially non-existent during test time, and such imperfect 2D evidence leads to erroneous adaptation. To overcome the above issues, we introduce CycleAdapt, which cyclically adapts two networks: a human mesh reconstruction network (HMRNet) and a human motion denoising network (MDNet), given a test video. In our framework, to alleviate high reliance on 2D evidence, we fully supervise HMRNet with generated 3D supervision targets by MDNet. Our cyclic adaptation scheme progressively elaborates the 3D supervision targets, which compensate for imperfect 2D evidence. As a result, our CycleAdapt achieves state-of-the-art performance compared to previous test-time adaptation methods. The codes are available at https://github.com/hygenie1228/CycleAdapt_RELEASE.

• The paper introduces CycleAdapt, a framework that cyclically refines a mesh reconstruction network and a motion denoising network to address domain gaps.
• It leverages self-supervised learning to generate reliable 3D supervision from noisy monocular video data in test-time adaptation scenarios.
• The approach achieves state-of-the-art performance on benchmarks like 3DPW using metrics such as MPJPE and PA-MPJPE, enhancing practical applications in AR and VR.

Summary of "Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh Reconstruction"

The paper "Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh Reconstruction" introduces a novel framework named CycleAdapt, aimed at improving the accuracy of 3D human mesh reconstruction in test-time adaptation scenarios. This work addresses the domain gap issue, which is a significant challenge due to discrepancies between training data, typically collected in controlled environments like MoCap datasets, and real-world test data.

CycleAdapt Framework

CycleAdapt employs a cyclic adaptation strategy that iteratively enhances two neural networks: the Human Mesh Reconstruction Network (HMRNet) and the Human Motion Denoising Network (MDNet). The core innovation lies in using MDNet to provide reliable 3D supervision targets for HMRNet, overcoming the limitations of traditional methods which heavily rely on 2D evidence such as human keypoints or silhouettes.

Components:

• HMRNet: Takes single images from a test video, predicts SMPL parameters, and reconstructs 3D human meshes. It is evolved from a pre-trained model with full supervision from 3D targets generated by MDNet, along with 2D evidence from test images.
• MDNet: Provides denoising to enhance the temporal coherence of human motion by refining noisy mesh sequences output from HMRNet, adapted through self-supervised learning due to lack of 3D ground truth during testing.

Cyclic Adaptation Strategy

The framework iterates over adaptation stages for both networks:

1. HMRNet Adaptation Stage: Focuses on refining mesh reconstructions using the 3D predictions from the previous MDNet output.
2. MDNet Adaptation Stage: Utilizes self-supervision to adapt the refinement of motion sequences, thus mitigating noise and inconsistency without direct 3D supervision.

The cyclic nature of this method allows continuous improvement of 3D supervisory data, progressively refining HMRNet's reconstructions by aligning them closely with real-world test video distributions.

Results and Impact

The framework achieves state-of-the-art performance on benchmark datasets like 3DPW and InstaVariety by significantly improving over traditional methods such as BOA and DynaBOA. This success is attributed to resolving the critical dependence on unreliable 2D evidence, as demonstrated by comprehensive quantitative metrics like MPJPE and PA-MPJPE.

Practical and Theoretical Implications

Practically, this method enhances applications in augmented reality, virtual reality, and video gaming where accurate and consistent 3D human modeling is essential. Theoretically, it introduces a scalable approach to model adaptation in novel environments without retraining from scratch.

Future Directions

This paper sets a precedent for exploring self-supervised methods in test-time model adaptation. Future research can expand this cyclic adaptation methodology to other domains where real-time adaptation to new environments is critical. Additionally, refining the self-supervised learning strategy for even greater independence from 2D evidence could further heighten performance.

In summary, CycleAdapt represents a significant advancement in test-time adaptation for 3D human mesh reconstruction, alleviating prior limitations and setting a new benchmark in handling domain shifts.