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arxivcs.CV2026-07-01

SPECSIA: Stylization Dataset for Novel-View Enhancement in Drawing-based 3D Animation

Kyuwon Kim, Sunjae Yoon, Chang D. Yoo

Generating animation from a single 2D drawing is challenging because the output must preserve character appearance while remaining plausible and temporally coherent under motion. Existing drawing-based 3D animation pipelines often use sample-wise 2D refinement to align animated renderings with the input image, but such optimization tends to overfit to the observed view and fails to correct projection-induced artifacts in novel views. To address this limitation, we introduce SPECSIA-15K, a paired stylization dataset containing 14,980 artifact-corrupted projection/refinement-target pairs from 1,498 3DBiCar characters. We further present DraViE (Drawing-based View Enhancement), a lightweight plug-and-play module trained with data-level priors to remove novel-view artifacts while preserving style and motion plausibility. Experiments show consistent gains in novel-view fidelity and temporal coherence with lower per-character adaptation cost than sample-wise fine-tuning.

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arxivcs.CV2026-07-23

GrainGS: Gradient-Decoupled Gaussian Splatting for Efficient Dynamic Novel View Synthesis

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arxivcs.CV2026-07-24

Visual Relocalization from Sparse Views in Aliased and Low-Texture Environments via Novel View Synthesis

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Visual localization becomes extremely challenging in planetary-like terrains characterized by low texture, perceptual aliasing, harsh illumination, and sparse, weakly overlapping viewpoints induced by forward rover motion and unconstrained driving directions. Under these conditio…

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arxivcs.CVcs.MMcs.RO2026-07-23

TransBiolab: A Real-World Multi-View Dataset of Cluttered Transparent Biomedical Objects

Ke Ma, Yifei Wang, Meng Wang, Tian Xia

Autonomous biomedical laboratories increasingly rely on visual perception to recognize, localize, and manipulate transparent plasticware, yet high-quality real-world datasets for this setting remain limited. The scarcity of domain-relevant data is particularly restrictive in clut…

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arxivcs.CV2026-07-22

Look Before You Edit: Attention-Guided Camera Placement and Multi-View Alignment for 3D Gaussian Splatting Editing

Jaeyeon Park, Taeho Kang, Youngki Lee

Text-driven 3D scene editing with 3D Gaussian Splatting (3DGS) typically applies a 2D diffusion editor to views rendered from fixed training cameras, limiting both the spatial coverage of edits and the user's freedom to target specific objects in complex scenes. We present LB-Edi…

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arxivcs.CV2026-07-22

ATSplat: Compact Feed-forward 3D Gaussian Splatting with Adaptive Token Expansion

Cho In, Jeonghwan Cho, Mijin Yoo, Gim Hee Lee, Seon Joo Kim

3D Gaussian Splatting (3DGS) achieves high-quality novel-view synthesis by optimizing freely placed primitives in 3D and adaptively densifying them in under-reconstructed regions. However, this scene-adaptive capacity allocation is largely lost in existing feed-forward 3DGS metho…

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