arXiv subjects

Junyi Ouyang

Publications and source records attributed to Junyi Ouyang.

3 recordsLinked to original sources

arXiv2026

XDG: Accelerated Visual Disambiguation

Visual aliasing, also known as the doppelganger problem, remains a key challenge for structure-from-motion (SfM): visually similar but physically distinct surfaces can produce incorrect image matches and degrade reconstruction quality. Previous work mitigates this issue with geometry-aware foundation-model features, but places a heavy transformer classifier on top of the backbone, making large-scale disambiguation expensive. We introduce XDG, an efficient visual disambiguation model designed for scalable SfM. Our key observation is that a 3D foundation model already performs the cross-view geometric reasoning necessary for visual disambiguation, so doppelganger classification should adapt the backbone representation directly rather than relearn pair reasoning in a separate heavy decoder. XDG fine-tunes Depth Anything 3 with lightweight LoRA adapters and repurposes its camera tokens as compact pair-level classification tokens. A compact MLP head predicts whether a candidate image pair observes the same 3D surface. Extensive experiments show that XDG provides a favorable accuracy-efficiency tradeoff: it remains competitive with the state-of-the-art disambiguation method across pairwise and reconstruction benchmarks and delivers more than a 3x inference speedup. On individual LaMAR scenes containing thousands of images, XDG saves more than 10 hours of visual disambiguation processing. Code is available at https://github.com/xtcpete/xdg.

cs.CV↗

arXiv2026

Hiding Directions, Leaking Structure: Breaking ArrowCloak through Low-Rank Structure

TEE-shielded inference keeps sensitive state in a trusted execution environment (TEE) while offloading linear algebra to an untrusted accelerator. Wang et al., in Game of Arrows (USENIX Security 2025), showed that five widely adopted lightweight defenses preserve vector directions and introduced ArrowMatch to exploit this leakage. They then proposed ArrowCloak, which adds a different multiple of one shared mask direction to each vector and bases its weight-recovery hardness argument on Learning with Errors (LWE). ArrowCloak successfully reduces ArrowMatch to near-black-box levels. In this paper, we revisit ArrowCloak from cryptographic and structural perspectives. Its LWE formulation does not by itself establish standard LWE hardness: the reduction direction, quantized arithmetic, and joint instance distribution do not meet the required conditions. Reusing one mask direction leaves a recoverable rank-one component across the released matrix. We exploit this structure with our proposed attack, an end-to-end, query-free recovery attack. Given a public checkpoint and the obfuscated weights, the attack removes the masking subspace, recovers the hidden one-to-one correspondence, and reconstructs protected weights without transformation secrets, victim queries, or fine-tuning data. Across six model-task pairs spanning classification, segmentation, and diffusion, the attack recovers 99.92%-100% of hidden vector correspondences. Reconstructed classification models achieve 94.39%-99.54% victim agreement and differ by at most 1.59 percentage points in accuracy; the recovered segmentation model achieves 98.35% output agreement. These findings suggest that lightweight protection should address both per-vector geometry and joint structure across released weights.

cs.CR↗

arXiv2026

DCARL: A Divide-and-Conquer Framework for Autoregressive Long-Trajectory Video Generation

Long-trajectory video generation is a crucial yet challenging task for world modeling primarily due to the limited scalability of existing video diffusion models (VDMs). Autoregressive models, while offering infinite rollout, suffer from visual drift and poor controllability. To address these issues, we propose DCARL, a novel divide-and-conquer, autoregressive framework that effectively combines the structural stability of the divide-and-conquer scheme with the high-fidelity generation of VDMs. Our approach first employs a dedicated Keyframe Generator trained without temporal compression to establish long-range, globally consistent structural anchors. Subsequently, an Interpolation Generator synthesizes the dense frames in an autoregressive manner with overlapping segments, utilizing the keyframes for global context and a single clean preceding frame for local coherence. Trained on a large-scale internet long trajectory video dataset, our method achieves superior performance in both visual quality (lower FID and FVD) and camera adherence (lower ATE and ARE) compared to state-of-the-art autoregressive and divide-and-conquer baselines, demonstrating stable and high-fidelity generation for long trajectory videos up to 32 seconds in length.

cs.CV↗