We present an empirical study of free-view video compression for dynamic scenes reconstructed with 3D Gaussian Splatting, examining how practical pipeline design choices affect reconstruction fidelity and storage efficiency. Rather than introducing new representations, we analyze how commonly used components, including temporal chunking, deformation-based reconstruction, and quantization-aware training, interact in practice. We observe that partitioning long sequences into shorter temporal segments, such as GOPs, simplifies optimization and improves reconstruction fidelity, but can introduce additional storage overhead. We further show that encouraging smooth motion vectors across both space and time produces deformation signals that are easier for standard video codecs to compress, leading to improved rate–distortion performance. When integrated into a unified pipeline, these design choices consistently benefit different deformation-based reconstruction methods. Across multiple datasets, our approach achieves 20% storage reduction compared with state-of-the-art methods while preserving or improving visual quality, and we discuss sources of variability and ambiguity in current training and evaluation protocols.
Mingyang Song, Yang Zhang, Siyu Tang et al.· International Conference on...· 0 citations
Generating realistic 3D human motions in real-time within interactive applications is key for animation, simulation, and humanoid robotics. While recent offline motion generation approaches offer precise control via text and kinematic constraints, they lack the inference speed required for interactive settings. Conversely, existing online methods enable real-time synthesis but often sacrifice controllability or struggle with complex text semantics and long-horizon goals due to limited context windows. In this work, we introduce ARDY, a streaming generation framework that bridges this gap by enabling high-fidelity motion generation controllable via online text prompts and flexible kinematic constraints. ARDY employs a hybrid representation that combines explicit root features with a latent body embedding, balancing precise trajectory control with efficient generative learning. We propose a two-stage autoregressive transformer denoiser that features variable history context and supports conditioning on flexible, long-horizon kinematic constraints. By training on a large-scale motion capture dataset and being directly conditioned on text labels and kinematic constraints sampled from ground truth poses, ARDY natively learns controllable generation that supports online prompting and flexible long-horizon goals. Extensive evaluations on the HumanML3D benchmark and the large-scale, high-fidelity Bones Rigplay dataset demonstrate ARDY's high motion quality and constraint adherence, validating the efficacy of our key architectural decisions. Finally, we demonstrate the method's practical versatility through an interactive demo featuring dynamic text control, diverse keyframe pose constraints, path following, and interactive locomotion control via mouse and keyboard. Supplementary video results, code, and model releases can be found at https://research.nvidia.com/labs/sil/projects/ardy/.
Kaifeng Zhao, Mathis Petrovich, Haotian Zhang et al.· ACM Transactions on Graphics· 1 citation