Closing the Loop: Training-Free Revisit Consistency for Autoregressive Generative Rendering
Recent conditional video generation models can convert 3D engine renderings like depth maps into photorealistic videos, but suffer from appearance inconsistencies when the camera revisits a location after context eviction in long-horizon autoregressive generation. This paper proposes a training-free method that leverages correspondences from the 3D engine: temporal correspondence retrieves pose-matched historical latent chunks into the KV cache as loop-closure memory, while spatial correspondence biases token-level attention toward geometrically corresponding regions. Evaluations on loop-closure trajectories from TartanAir and TartanGround show improved revisit consistency without sacrificing video quality, outperforming existing training-free baselines.
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[Submitted on 23 Jul 2026]
Title:Closing the Loop: Training-Free Revisit Consistency for Autoregressive Generative Rendering
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Abstract:Recent conditional video generation models have shown promising potentials to transform 3D engine renderings, such as depth maps and untextured geometry, into photorealistic videos for gaming and immersive content creation. These applications require long-horizon auto-regressive generation that continuously synthesizes new frames while preserving a persistent 3D world. Auto-regressive generators synthesize video chunk by chunk with a bounded KV cache, so when the camera revisits a location after its context has been evicted, the model often regenerates inconsistent appearance, even though the conditioning renderings (e.g., depth) remain perfectly aligned with the underlying this http URL address this revisit inconsistency without any post-training by exploiting correspondences the 3D engine already provides: temporal correspondence retrieves pose-matched historical latent chunks into the KV cache as loop-closure memory, while spatial correspondence from camera pose and depth reprojection biases token-level attention toward geometrically corresponding regions of the retrieved chunks. We demonstrate our method on loop-closure trajectories mined from TartanAir and TartanGround dataset to mirror complicate real-world application scenarios, where it outperforms existing training-free baselines on revisit consistency without losing overall video quality. Project Page: this https URL
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Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.21848 [cs.CV]
(or arXiv:2607.21848v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.21848
arXiv-issued DOI via DataCite (pending registration)
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From: Wenchao Ma [view email] [v1] Thu, 23 Jul 2026 22:33:52 UTC (2,701 KB)
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