Self-Supervised Pre-Training for Efficient 3D Human Understanding

核心概念

A self-supervised pre-training approach that leverages cross-view and cross-pose image pairs to learn priors about 3D human structure and motion, enabling efficient fine-tuning on a variety of downstream human-centric vision tasks.

要約

The authors propose a self-supervised pre-training approach for 3D human understanding tasks. The key idea is to leverage pairs of images depicting the same person from different viewpoints (cross-view) or in different poses (cross-pose) to learn priors about 3D human structure and motion.

The pre-training process involves a masked image modeling objective, where parts of the first image in a pair are masked, and the model is trained to reconstruct the masked regions using the visible parts of the first image as well as the second image in the pair. This allows the model to learn to leverage both geometric and motion cues to reason about the 3D human body.

The authors pre-train two models, one focused on full-body tasks (CroCo-Body) and one on hand-centric tasks (CroCo-Hand), using a diverse set of multi-view and video datasets. They then fine-tune these pre-trained models on a range of downstream tasks, including body and hand mesh recovery, dense pose estimation, gesture classification, and grasp recognition.

The results show that the proposed self-supervised pre-training approach outperforms both random initialization and pre-training on ImageNet or other self-supervised methods when fine-tuning on the target human-centric tasks. The authors also demonstrate the data efficiency of their approach, showing that the pre-trained models can achieve strong performance even with limited fine-tuning data.

統計

"The model is trained by optimizing: min θ,ϕ Σ(pt,pt′)∈Dpose || ˆptθ,ϕ −pt||2 + Σ(pv,pw)∈Dview || ˆpvθ,ϕ −pv||2."
"We rely on a mix of diverse human-centric datasets namely 3DPW, Posetrack, PennAction, JRDB, MARS and AIST."
"HUMBI contains more than 300 subjects, with a wide range of age, body-shapes, and clothing variety but a restricted set of poses, while AIST sequences are captured from only 40 subjects with 9 different camera viewpoints, with plain clothing, but contain a great diversity of poses, with e.g. dance moves from professional performers."

引用

"We hypothesize that the most common pre-training strategy of relying on general purpose, object-centric image datasets such as ImageNet, is limited by an important domain shift."
"To leverage large amounts of data and scale to large models, self-supervised pre-training methods such as contrastive learning and masked signal modeling have been developed."
"Unlike MAE which operates on individual images, in our case pairs of images of human bodies are leveraged."

抽出されたキーインサイト

Cross-view and Cross-pose Completion for 3D Human Understanding

by Matt... 場所 arxiv.org 04-19-2024

https://arxiv.org/pdf/2311.09104.pdf

深掘り質問

How could the proposed self-supervised pre-training approach be extended to handle multi-person scenes

The proposed self-supervised pre-training approach can be extended to handle multi-person scenes by incorporating additional information and constraints during the pre-training phase. One way to achieve this is by introducing a new pretext task that focuses on learning representations that capture interactions between multiple individuals in a scene. This task could involve predicting the relative positions, poses, or actions of different people within the same image or video frame. By training the model to understand and predict these interactions, it can learn to differentiate between different individuals, understand their spatial relationships, and capture the dynamics of multi-person scenes.

What other types of auxiliary tasks or pretext objectives could be explored to further improve the learned representations for 3D human understanding

To further improve the learned representations for 3D human understanding, additional auxiliary tasks or pretext objectives can be explored. One potential task could involve predicting the 3D structure of the human body from 2D images, which would require the model to learn depth information and spatial relationships. Another task could focus on predicting the motion or dynamics of human poses from sequential images or videos, enhancing the model's ability to understand and interpret human movements. Additionally, tasks related to object manipulation, scene understanding, or social interactions could be incorporated to provide a more comprehensive understanding of human behavior in various contexts.

How could the insights from this work on leveraging cross-view and cross-pose information be applied to other domains beyond human perception, such as general 3D scene understanding

The insights from leveraging cross-view and cross-pose information for 3D human understanding can be applied to other domains beyond human perception, such as general 3D scene understanding. By incorporating multiple viewpoints and poses of objects or scenes, models can learn to capture the spatial relationships, shapes, and dynamics of 3D entities more effectively. This approach can be beneficial in tasks like object recognition, scene reconstruction, and spatial reasoning, where understanding objects from different perspectives and angles is crucial. By training models on diverse viewpoints and poses, they can develop a more robust and comprehensive understanding of 3D scenes and objects, leading to improved performance in various 3D-related tasks.