Chen Kai

Mengtian Li, Shengxiang Yao, Chen Kai et al.

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.

Li Pan, Lv Peizhuo, Chen Kai et al.

Compared to traditional neural networks with a single output channel, a multi-exit network has multiple exits that allow for early outputs from the model's intermediate layers, thus significantly improving computational efficiency while maintaining similar main task accuracy. Existing model stealing attacks can only steal the model's utility while failing to capture its output strategy, i.e., a set of thresholds used to determine from which exit to output. This leads to a significant decrease in computational efficiency for the extracted model, thereby losing the advantage of multi-exit networks. In this paper, we propose the first model stealing attack against multi-exit networks to extract both the model utility and the output strategy. We employ Kernel Density Estimation to analyze the target model's output strategy and use performance loss and strategy loss to guide the training of the extracted model. Furthermore, we design a novel output strategy search algorithm to maximize the consistency between the victim model and the extracted model's output behaviors. In experiments across multiple multi-exit networks and benchmark datasets, our method always achieves accuracy and efficiency closest to the victim models.