3 accepted papers
Diffusion Transformers (DiTs) are increasingly adopted for long-video generation, yet inference is dominated by the quadratic cost of 3D full attention. Sparse attention mitigates this bottleneck by exploiting *token-pair redundancy* and pruning query-key interactions. Nevertheless, its effectivenes…
Generating high-quality long videos with Diffusion Transformers (DiTs) faces significant latency due to computationally intensive attention mechanisms. For instance, generating an 8s 720p video (110K tokens) with HunyuanVideo requires around 600 PFLOPs, with attention computations consuming about 50…
3D articulated objects are inherently challenging for manipulation due to the varied geometries and intricate functionalities associated with articulated objects. Point-level affordance, which predicts the per-point actionable score and thus proposes the best point to interact with, has demonstrated…