DOVE: Learning Deformable 3D Objects by Watching Videos (2107.10844v2)

Abstract: Learning deformable 3D objects from 2D images is often an ill-posed problem. Existing methods rely on explicit supervision to establish multi-view correspondences, such as template shape models and keypoint annotations, which restricts their applicability on objects "in the wild". A more natural way of establishing correspondences is by watching videos of objects moving around. In this paper, we present DOVE, a method that learns textured 3D models of deformable object categories from monocular videos available online, without keypoint, viewpoint or template shape supervision. By resolving symmetry-induced pose ambiguities and leveraging temporal correspondences in videos, the model automatically learns to factor out 3D shape, articulated pose and texture from each individual RGB frame, and is ready for single-image inference at test time. In the experiments, we show that existing methods fail to learn sensible 3D shapes without additional keypoint or template supervision, whereas our method produces temporally consistent 3D models, which can be animated and rendered from arbitrary viewpoints.

• The paper presents DOVE, an unsupervised framework that learns 3D shape, pose, and texture of deformable objects from monocular videos.
• It employs temporal coherence and symmetry constraints to resolve pose ambiguities, enhancing reconstruction efficiency and robustness.
• Empirical evaluations on a novel 3D Toy Bird dataset show that DOVE achieves competitive accuracy and produces realistic, temporally consistent meshes.

An Expert Review of "DOVE: Learning Deformable 3D Objects by Watching Videos"

The paper "DOVE: Learning Deformable 3D Objects by Watching Videos" presents a significant advancement in the field of 3D reconstruction, specifically targeting the challenging task of reconstructing deformable objects using uncalibrated monocular video data. This work stands out in the landscape of unsupervised 3D learning by addressing and mitigating two primary challenges: the ambiguity of 3D shape inference from 2D video data and the requisite high cost of explicit geometric supervision found in many existing methods.

Methodological Contributions

The authors introduce the DOVE model, which effectively leverages temporal information inherent in videos to establish correspondences that static images fail to provide. By solving symmetry-induced pose ambiguities and using flows to enforce temporal coherence, DOVE can automatically learn to disentangle 3D shape, articulated pose, and texture from individual frames. This methodology represents a shift from reliance on explicit training annotations, such as keypoints and templates, to a more natural unsupervised video-based learning paradigm.

A noteworthy contribution of the paper is the model's ability to manage viewpoint ambiguities, often prominent in image-based methods. Unlike approaches that require extensive viewpoint sampling, DOVE identifies symmetries that restrict pose ambiguity to a finite set of symmetries. This reduces computational redundancy and improves the model's efficiency. Furthermore, the paper proposes a hierarchical shape model that captures intra-class variability without explicit geometric supervision, supporting the model's capacity to generalize across different object instances within a category.

Empirical Demonstration

The empirical validation of DOVE against existing methodologies is rigorous. The evaluation includes the creation of a novel 3D Toy Bird Dataset, offering a unique testbed with ground-truth scans for performance benchmarking—something that has been notably absent in this research area. The results presented in the paper underscore DOVE's superiority in reconstructing temporally consistent and realistic 3D models that retain articulable features suitable for applications requiring dynamic representations.

Quantitatively, DOVE demonstrates competitive reconstruction accuracy, measured by Chamfer Distance against state-of-the-art baseline models finetuned with similar data. Qualitatively, DOVE-produced meshes exhibit accuracy and consistency, highlighting its potentials in practical applications such as animation and synthetic data generation.

Implications and Future Prospects

The implications of DOVE extend both theoretically and practically. Theoretically, it sets a precedent for more advanced unsupervised learning paradigms in 3D reconstruction, showcasing that viable models can be trained with minimal supervised constraints. Practically, this opens avenues for a wider range of applications—particularly in fields requiring realistic 3D content creation from everyday videos without complex setup or infrastructure.

Future developments stemming from DOVE could include exploring the application to broader categories of deformable objects or refining the model's ability to handle real-time data streams. Furthermore, extending this framework to richer datasets with diverse environments could further establish its robustness and adaptability across different real-world settings.

In conclusion, DOVE is a compelling contribution to the field, offering a novel solution to the challenges of learning 3D deformable objects by maximizing the utility of available video data. Its success could pivot future research efforts towards more generalized unsupervised learning frameworks, reducing the dependency on costly annotated datasets and making complex 3D tasks accessible across varied domains.