VINECS: Video-based Neural Character Skinning (2307.00842v1)

Abstract: Rigging and skinning clothed human avatars is a challenging task and traditionally requires a lot of manual work and expertise. Recent methods addressing it either generalize across different characters or focus on capturing the dynamics of a single character observed under different pose configurations. However, the former methods typically predict solely static skinning weights, which perform poorly for highly articulated poses, and the latter ones either require dense 3D character scans in different poses or cannot generate an explicit mesh with vertex correspondence over time. To address these challenges, we propose a fully automated approach for creating a fully rigged character with pose-dependent skinning weights, which can be solely learned from multi-view video. Therefore, we first acquire a rigged template, which is then statically skinned. Next, a coordinate-based MLP learns a skinning weights field parameterized over the position in a canonical pose space and the respective pose. Moreover, we introduce our pose- and view-dependent appearance field allowing us to differentiably render and supervise the posed mesh using multi-view imagery. We show that our approach outperforms state-of-the-art while not relying on dense 4D scans.

• The paper introduces an end-to-end trainable system that automates rigging and skinning of 3D human avatars directly from multi-view video inputs.
• It leverages a coordinate-based MLP to compute pose-dependent skinning weights, enabling realistic deformations without dense 3D scans.
• The approach outperforms state-of-the-art methods by reducing reconstruction errors and effectively handling challenging dynamic poses and loose clothing.

Insights on Video-based Neural Character Skinning (VINECS)

The paper entitled "VINECS: Video-based Neural Character Skinning" presents an innovative approach to automating the rigging and skinning of 3D human avatars directly from multi-view video data. This is a significant advancement in the field of computer graphics and vision, where manual rigging and skinning are typically labor-intensive and require substantial expertise. Traditional methods often fail to accommodate dynamic and highly articulated poses due to reliance on static skinning weights or dense scans of 3D characters in different configurations. VINECS addresses these limitations through a novel methodology that leverages multi-view video to create fully rigged characters with pose-dependent skinning weights.

Technical Contributions

VINECS introduces a coordinate-based multi-layer perceptron (MLP) model to learn skinning weights that vary with pose, enabling the generation of realistic deformations in character animations. This is achieved without the necessity for dense 3D scans or manual adjustments, making the process more accessible and efficient. Key contributions of the paper include:

1. End-to-End Trainable System: The system can generate animation-ready explicit character meshes directly from video inputs, incorporating both rigging and pose-dependent skinning.
2. Pose-Dependent Skinning Formulation: The use of an MLP allows for continuous sampling of skinning weights across the 3D canonical space, facilitating robust multi-resolution character skinning.
3. Differentiable Rendering with Supervision: The approach incorporates a unique appearance model that provides pose- and view-dependent rendering, enhancing weak supervision using silhouette and rendering losses.

Results and Comparative Analysis

VINECS has been evaluated against existing state-of-the-art methods such as SCANimate and SNARF, which traditionally require dense point cloud data for training. The proposed method demonstrates superior performance in achieving lower reconstruction errors (measured in Chamfer distance and Hausdorff distance) across multiple test subjects, even when compared solely on multi-view video data. Importantly, VINECS illustrates improved accuracy over SCANimate in subjects with loose clothing, highlighting its robustness to a variety of clothing types.

Discussion of Implications

The practical implications of VINECS are vast, as it streamlines the creation of animatable 3D characters from easily obtainable video inputs. This can significantly lower barriers for industries relying on character animations, such as game development, film production, and virtual reality applications. From a theoretical standpoint, VINECS contributes to the ongoing dialogue about the capabilities and extensions of neural networks in graphics, particularly in generating realistic, articulate human figures solely from visual data.

Future Developments

Future exploration might extend VINECS by integrating facial expression modeling or optimizing computational efficiency using advanced neural network architectures like hashgrids. Additionally, further research could focus on simultaneous rigging, skinning, and pose tracking, enhancing the inflow of high-quality animation-ready representations of diverse character models.

To conclude, the contributions of "VINECS: Video-based Neural Character Skinning" not only enhance the automation of the rigging and skinning process but also provide a foundation for future explorations in creating dynamic 3D character animations using neural methods that operate directly on multi-view video inputs, marking a substantive step forward in computer-generated character production.