DIScene is capable of generating complex 3D scene with decoupled objects and clear interactions. Leveraging a learnable Scene Graph and Hybrid Mesh-Gaussian representation, we get 3D scenes with superior quality. DIScene can also flexibly edit the 3D scene by changing interactive objects or their attributes, benefiting diverse applications.
We introduce Bi-TTA, a method that leverages spatial and temporal consistency for appropriate self-supervision, coupled with novel
prospective and retrospective adaptation strategies, enabling superior adaptation ability of
pre-trained rPPG models to the target domain using only unannotated, instance-level target data.
We present LucidDreamer, a text-to-3D generation framework, to distill high-fidelity textures and shapes from pretrained 2D
diffusion models with a novel Interval Score Matching objective and an advanced 3D distillation pipeline.
Together, we achieve superior 3D generation results with photorealistic quality in a short training time.