Human Mesh Recovery from Multiple Shots (2012.09843v1)

Abstract: Videos from edited media like movies are a useful, yet under-explored source of information. The rich variety of appearance and interactions between humans depicted over a large temporal context in these films could be a valuable source of data. However, the richness of data comes at the expense of fundamental challenges such as abrupt shot changes and close up shots of actors with heavy truncation, which limits the applicability of existing human 3D understanding methods. In this paper, we address these limitations with an insight that while shot changes of the same scene incur a discontinuity between frames, the 3D structure of the scene still changes smoothly. This allows us to handle frames before and after the shot change as multi-view signal that provide strong cues to recover the 3D state of the actors. We propose a multi-shot optimization framework, which leads to improved 3D reconstruction and mining of long sequences with pseudo ground truth 3D human mesh. We show that the resulting data is beneficial in the training of various human mesh recovery models: for single image, we achieve improved robustness; for video we propose a pure transformer-based temporal encoder, which can naturally handle missing observations due to shot changes in the input frames. We demonstrate the importance of the insight and proposed models through extensive experiments. The tools we develop open the door to processing and analyzing in 3D content from a large library of edited media, which could be helpful for many downstream applications. Project page: https://geopavlakos.github.io/multishot

Human Mesh Recovery from Multiple Shots

The paper "Human Mesh Recovery from Multiple Shots" presents an innovative approach for reconstructing 3D human meshes from video sequences that include abrupt shot changes, particularly in edited media such as movies. The authors address the challenge posed by these discontinuities, which are typically treated as barriers by existing human 3D understanding frameworks, thereby reducing movies to fragmented scenes. The research leverages the insight that despite the abrupt shot changes, the underlying 4D structure of the scene evolves smoothly. This observation enables the treatment of different shots as multi-view signals that provide strong cues for recovering the 3D states of actors.

Methodology and Results

The paper proposes a multi-shot optimization framework that enables more accurate 3D reconstructions and the extraction of longer 3D pose sequences. These sequences can serve as valuable training data for deep learning models aimed at single-view and temporal human mesh recovery. The authors introduce a pioneering transformer-based model for video sequence analysis, which significantly improves robustness in handling missing observations due to shot changes in movies. The experimental results illustrate that incorporating the multi-view signals from multiple shots effectively enhances the quality of 3D reconstructions. This is evidenced by the cross-shot PCK metric used for evaluation, which demonstrates superior performance compared to single-shot baselines.

Practical and Theoretical Implications

On the practical side, this approach opens up new avenues for leveraging a large corpus of edited media to enrich training datasets for various applications, including video understanding and action recognition. Theoretically, treating sudden shot changes as multi-view signals rather than independent disruptions offers a novel perspective that can influence future research in dynamic scene analysis and 3D reconstruction in media contexts. This methodology also has implications for improving the sustainability and versatility of models trained on real-world, challenging datasets, as demonstrated by the robustness of models trained on the newly introduced Multi-Shot AVA dataset.

Future Directions

The paper's exploration of a transformer-based architecture for temporal sequence analysis is particularly noteworthy, as it accommodates non-continuous identifications without requiring complete temporal sequences. This adaptability suggests that transformers could be further explored in AI research for other applications requiring self-attention mechanisms across temporal data. Future work could explore optimizing multi-shot recovery frameworks to extract richer data that could benefit various downstream tasks in AI, such as enhancing human-machine interaction and improving autonomous systems' ability to interpret human activities accurately.

In conclusion, while the paper refrains from portraying its contributions as revolutionary, it undeniably contributes substantial advancements in addressing the long-standing issue of discontinuities in edited media. This work paves the way for further exploration and exploitation of such multi-shot datasets, potentially transforming approaches to 3D human pose and shape estimation in the field of AI.