EntroKine3D: Shared Kinetic-Profile Supervision for Single-Reference 3D Dance Camera Synthesis

Automatic 3D dance camera trajectory synthesis should support diverse artistic interpretations of the same choreography and accompanying music through variations in viewpoint, framing, and visual rhythm. However, currently available training data only provide one authored camera trajectory for each exact performance, and this observation is one accepted realization rather than the unique valid answer, making it difficult for instance-level supervision to characterize plausible variation across camera trajectories while preserving high-fidelity motion. In this paper, we propose EntroKine3D, a novel framework that supplements single-reference supervision with shared kinetic-profile constraints pooled from related performance states for high-quality stochastic synthesis. Specifically, to address the single-reference supervision challenge, we design an Entropy-calibrated Kinetic Profile Modeling (E-KPM) module to supervise generated camera windows against structured population-level kinetic references built from single-reference camera-motion statistics. E-KPM aligns continuous kinetic distributions via Sinkhorn divergence and matches discrete motion-state occupancy, condition-dependent allocation, and temporal transitions. To balance trajectory diversity with kinetic high-fidelity, we extend entropy calibration to further match profile uncertainty and the information that the past performance state provides about future camera-motion states to reference statistics under finite-sample, finite-step optimization. Furthermore, we incorporate latent consistency distillation as an efficient instantiation for quick four-step generation. On the DCM dataset, EntroKine3D achieves FID-K/FID-S of 4.342±0.22/0.398±0.03 and DIV-K of 3.072±0.12, close to the held-out reference value 3.290. Same-condition evaluations show that entropy calibration yields diverse yet faithful camera trajectories under one performance, while four-step generation achieves a 9.39× model-level speedup over the 50-step teacher.