AcrossVAM2.0：用于参数高效视频扩散适配的冲突感知点监督

# AcrossVAM2.0: Conflict-Aware Point Supervision for Parameter-Efficient Video Diffusion Adaptation
Source: [https://arxiv.org/abs/2610.14693](https://arxiv.org/abs/2610.14693)
[View PDF](https://arxiv.org/pdf/2610.14693)

> Abstract:Explicit point correspondence is attractive for adapting video generators: it describes how visible surfaces move, but can overwrite the appearance prior learned by a large pretrained model\. We study this tension in a 5\.01B\-parameter text\-to\-video diffusion transformer\. ACROSSVAM2\.0 combines a confidence\-weighted track\-and\-visibility head, zero\-initialized residual feedback, and asymmetric PCGrad that removes only the auxiliary component opposing the primary flow\-matching gradient on shared LoRA parameters\. The design preserves the generator exactly at initialization and protects its primary update during adaptation\. In a four\-way ablation trained for 1,000 steps with three independent seeds, the full method lowers KLT forward\-backward cycle error in all three seeds relative to a matched flow\-only continuation \(mean seed\-level difference \-0\.00711, SD 0\.00505\), while robust sharpness remains positive in all three\. An expanded 24\-prompt evaluation \(216 videos\) yields 9\.7% lower aggregate CoTracker3 cycle error than the matched control and near\-matched performance on six VBench dimensions; however, the seed\-level tracking effect is heterogeneous and I3D\-FVD regresses from 0\.1824 to 0\.2022\. These results support a bounded claim: conflict\-aware adaptation improves correspondence consistency without broad perceptual degradation, but does not uniformly improve video quality\. We release equations, pseudocode, matched outputs, source tables, and artifact provenance\.

## Submission history

From: Nan Wu \[[view email](https://arxiv.org/show-email/8edcfecf/2610.14693)\] **\[v1\]**Sat, 10 Oct 2026 08:12:08 UTC \(4,164 KB\)