AniGaussian: Animatable Gaussian Avatar with Pose-guided Deformation

Recent advancements in Gaussian-based human body reconstruction have achieved notable success in creating animatable avatars. However, there are ongoing challenges to fully exploit the SMPL model's prior knowledge and enhance the visual fidelity of these models to achieve more refined avatar reconstructions. In this paper, we introduce AniGaussian which addresses the above issues with two insights. First, we propose an innovative pose guided deformation strategy that effectively constrains the dynamic Gaussian avatar with SMPL pose guidance, ensuring that the reconstructed model not only captures the detailed surface nuances but also maintains anatomical correctness across a wide range of motions. Second, we tackle the expressiveness limitations of Gaussian models in representing dynamic human bodies. We incorporate rigid-based priors from previous works to enhance the dynamic transform capabilities of the Gaussian model. Furthermore, we introduce a split-with-scale strategy that significantly improves geometry quality. The ablative study experiment demonstrates the effectiveness of our innovative model design. Through extensive comparisons with existing methods, AniGaussian demonstrates superior performance in both qualitative result and quantitative metrics.

Model Stealing Attack against Multi-Exit Networks

Compared to traditional neural networks with a single exit, a multi-exit network has multiple exits that allow for early output from intermediate layers of the model, thus bringing significant improvement in computational efficiency while maintaining similar recognition accuracy. When attempting to steal such valuable models using traditional model stealing attacks, we found that conventional methods can only steal the model's classification function while failing to capture its output strategy. This results in a significant decrease in computational efficiency for the stolen substitute model, thereby losing the advantages of multi-exit networks.In this paper, we propose the first model stealing attack to extract both the model function and output strategy. We employ bayesian changepoint detection to analyze the target model's output strategy and use performance loss and strategy loss to guide the training of the substitute model. Furthermore, we designed a novel output strategy search algorithm that can find the optimal output strategy to maximize the consistency between the victim model and the substitute model's outputs. Through experiments on multiple mainstream multi-exit networks and benchmark datasets, we thoroughly demonstrates the effectiveness of our method.