Figureure 2 · : Proprio method overviewFigure 2: Proprio method overview. For a generated latent video, Proprio computes a per-video self-scoring signal by perturbing the latent at multiple timesteps, measuring denoising residuals with the frozen generator, weighting timesteps by inverse variance, and emphasizing informative motion regions with a dynamic spatiotemporal mask. The resulting score can either be used for ranking and selection, self-refining or both. The VAE encoder is omitted for brevity.这张图概括 Proprio 的整体方法流程。阅读时先看模块之间传递的训练信号,再看作者如何把目标拆成可优化的子问题。Figureure 1 · : Proprio enables a video generator to evaluate and improve its own geFigure 1: Proprio enables a video generator to evaluate and improve its own generation. Left: qualitative refinement comparisons for TurboWan2.2. Right: Proprio computes a latent self-score from the model’s denoising residual and uses it for either sample selection or inference-time refinement.这张图/表用于判断 Proprio 的实验收益来自哪里。重点看替换、消融或跨模型设置下趋势是否一致,而不是只看单个最高分。