Temporal Residual Jacobians For Rig-free Motion Transfer (2407.14958v1)

Abstract: We introduce Temporal Residual Jacobians as a novel representation to enable data-driven motion transfer. Our approach does not assume access to any rigging or intermediate shape keyframes, produces geometrically and temporally consistent motions, and can be used to transfer long motion sequences. Central to our approach are two coupled neural networks that individually predict local geometric and temporal changes that are subsequently integrated, spatially and temporally, to produce the final animated meshes. The two networks are jointly trained, complement each other in producing spatial and temporal signals, and are supervised directly with 3D positional information. During inference, in the absence of keyframes, our method essentially solves a motion extrapolation problem. We test our setup on diverse meshes (synthetic and scanned shapes) to demonstrate its superiority in generating realistic and natural-looking animations on unseen body shapes against SoTA alternatives. Supplemental video and code are available at https://temporaljacobians.github.io/ .

• The paper introduces the innovative TRJ approach that eliminates rigging by integrating spatial Jacobian fields with neural ODEs for temporal coherence.
• The methodology uses two coupled neural networks to predict local affine transformations and residual corrections, outperforming baselines with lower error rates.
• The results demonstrate significant implications for character animation by streamlining production and enabling realistic motion transfers over extended sequences.

Temporal Residual Jacobians for Rig-free Motion Transfer

The paper "Temporal Residual Jacobians for Rig-free Motion Transfer" by Sanjeev Muralikrishnan et al. presents an innovative method to perform rig-free, data-driven motion transfer using a novel representation called Temporal Residual Jacobians (TRJ). This approach alleviates the need for rigged models or intermediate shape keyframes, thereby enabling the direct transfer of long motion sequences to diverse, unrigged target shapes.

Methodology

Central to the proposed methodology are two coupled neural networks, one for local geometric changes and the other for temporal changes. These networks are jointly trained and supervised using 3D positional information. The process is driven by two key steps: spatial integration using a differentiable Poisson solver, and temporal integration utilizing Neural Ordinary Differential Equations (Neural ODEs).

Spatial Integration

The spatial component employs Neural Jacobian Fields (NJF) to predict local affine transformations (Jacobians) for each triangle in the target mesh. Given a shape and a set of joint angles describing the pose, NJF predicts changes in local Jacobians, which are later integrated over the entire shape using a Poisson solve to maintain geometric consistency.

Temporal Integration

For temporal coherence, the authors introduce the concept of Residual Jacobians, which are predicted by a Neural ODE. This residual strategy allows for the correction of base predictions from NJF and maintains temporal continuity, mitigating issues such as drift over long sequences. A multi-head attention mechanism encodes both current and previous window frames to feed into the ODE, which ensures the temporal coherence of the predicted motion.

Results

The authors tested their method on a range of datasets, including synthetic data, scanned shapes, and various motion categories from the AMASS, COP3D, and 4DComplete datasets. The results demonstrate that TRJ consistently produces realistic animations. The method is particularly effective at handling long sequences, which is a noted advantage over alternative baseline methods.

Quantitative Evaluation

The paper provides a detailed quantitative comparison, evaluating metrics such as vertex-to-vertex error, L2 error of predicted Jacobians, and angular error of normals across various motion categories like jumping, running, punching, walking, and dancing. The TRJ method consistently outperforms baselines, including VertexODE and an extended NJF approach, showing lower error rates and better generalization capabilities.

Implications

The TRJ method has significant implications in the domain of character animation, offering a flexible and efficient alternative to traditional rigging. By enabling realistic and natural motion transfers without the need for extensive pre-processing, TRJ can streamline animation workflows and broaden the scope for creative applications, particularly in areas where manual rigging and keyframing are impractical or resource-intensive.

Future Directions

Looking ahead, several avenues for future research are suggested:

1. Incorporation of Physical Constraints: Current limitations include possible self-intersections in the animated mesh due to the absence of physical constraints. Integrating physics-based constraints could improve the realism and robustness of the generated animations.
2. Enhanced Drift Control: While TRJ reduces error accumulation over long sequences, further techniques could be developed to eliminate drift entirely, possibly by integrating keyframe-based constraints.
3. Improving Correspondence: The current implicit correspondence mechanism may be prone to errors. Future work could focus on improving this aspect, perhaps by allowing manual intervention or incorporating additional semantic features.

Conclusion

The TRJ framework offers a compelling blend of flexibility, efficiency, and generalizability for motion transfer applications. By leveraging local geometric and temporal changes, this method sets a new standard in rig-free animation, paving the way for further advancements in this field. This novel approach is poised to significantly impact both academic research and practical applications in computer-generated animation.

Link to supplemental video and code: Temporal Residual Jacobians