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SILSA: Sliding-Window Slice Latents for Topology-Preserving High-Resolution 3D Generation

Paper recorded by Signals 4 on 2026-10-01 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-01 on arXiv · recorded by Signals 4 on 2026-10-02

Category: cs.AI · 人工智能 · first seen 2026-10-02

Abstract

High-resolution 3D generation increasingly relies on voxel latents and multi-stage pipelines that first predict active structure and then synthesize local geometry. While effective, this design fragments continuous surfaces into many local tokens, inflates generation cost, and often weakens topological consistency for thin or highly connected shapes. We introduce SILSA, a topology-aware 3D generat

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Cite this page: SILSA: Sliding-Window Slice Latents for Topology-Preserving High-Resolution 3D Generation: the #5 most recent of 500 cs.AI papers we have recorded (as of 2026-10-01). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/silsa-sliding-window-slice-latents-for-topology-preserving-high-resolution-3d-ge.html
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