CC-MotionGen: Audio-Conditioned Latent Motion Diffusion with Validation-Based Candidate Selection

CC-MotionGen is a diffusion-based generative system that produces time-indexed motion trajectories conditioned on audio features and optional high-level context. The system targets phrase-level generation: it consumes precomputed audio feature tensors and precomputed motion latents, trains a temporal one-dimensional U-Net denoiser under a Gaussian diffusion process, and performs inference by sampling multiple candidate futures and selecting the best output using a two-stage validation pipeline. The validation pipeline first applies deterministic plausibility constraints, called sanity checks, that reject physically implausible trajectories. It then applies a heuristic musicality scorer that ranks the remaining candidates according to alignment with beat structure, energy envelope, phrase boundaries, and timbral “tension” cues derived from audio. This paper provides a research-grade description of CC-MotionGen grounded in the implementation, including the on-disk data schema, temporal alignment strategy, conditioning interfaces, U-Net construction and skip bookkeeping, diffusion schedules and DDIM sampling with classifier-free guidance, training loop mechanics such as mixed precision and learning-rate scheduling, and the inference-time speculative sampling workflow with monitoring metrics. Mathematical operations are defined in plain language without symbolic notation, with an emphasis on invariants, failure modes, computational characteristics, and extensibility points suitable for subsequent empirical evaluation.