VfxDB: A Visual Effects Volume Dataset and Benchmark for VDB-Native Generative Modeling

摘要

Volumetric effects such as smoke, fire, dust, and explosions are central to Visual Effects (VFX) production and are commonly represented as sparse, high-resolution VDB/OpenVDB sequences with dynamic topology. Despite rapid progress in diffusion-based 3D generation, work on sparse volumetric sequences remains difficult to compare and reproduce, due to the lack of large-scale, well organized datasets and standardized evaluation protocols. In this paper, we introduce a 1-million-sample VFX sequence of VDB dataset with standardized preprocessing, consistent metadata, and protocol-ready splits, together with a reproducible benchmark suite for both static volume generation and sequence volumes generation. We further provide an end-to-end evaluation pipeline and a scalable diffusion training framework, enabled by our Atomic-Continuous prior, which addresses the distributional mismatch between vanilla diffusion models and the intrinsic sparsity of VDB data. Our release establishes a practical infrastructure for reproducible research and systematic progress tracking in sparse volumetric sequence generation. Project website: https://vfxdb-official.github.io/VfxDB/.