4 accepted papers
Human-centric generative models are becoming increasingly popular, giving rise to various innovative tools and applications, such as talking face videos conditioned on text or audio prompts. The core of these capabilities lies in powerful pre-trained foundation models, trained on large-scale, high-q…
Consistent pose‐driven character animation has achieved remarkable progress in single‐character scenarios. However, extending these advances to multi‐character settings is non‐trivial, especially when position swap is involved. Beyond mere scaling, the core challenge lies in enforcing correct Identi…
Existing text-to-image models often rely on parame- ter fine-tuning techniques such as Low-Rank Adaptation (LoRA) to customize visual attributes. However, when com- bining multiple LoRA models for content-style fusion tasks, unstructured modifications of weight matrices often lead to undesired featu…
We propose Neural Robust Distilled Feature Fields (RD-NeRF) for achieving robust 3D semantic feature distillation and 3D consistent scene segmentation with sparse-view labels. Specifically, we introduce a two-stage pipeline. In the distillation stage, we employ the pre-trained image feature extracto…