{"total":11,"items":[{"citing_arxiv_id":"2606.22481","ref_index":264,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Lighting-Consistent Object Transfer Across Radiance Fields","primary_cat":"cs.GR","submitted_at":"2026-06-21T12:50:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Diffusion-based per-view harmonization for lighting-consistent object transfer between 3DGS scenes, using heterogeneous training data and final 3D consolidation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.31466","ref_index":88,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"VolFill: Single-View Amodal 3D Scene Reconstruction with Volumetric Flow Matching","primary_cat":"cs.CV","submitted_at":"2026-05-29T15:59:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"VolFill uses a hybrid 3D VAE to compress sparse truncated unsigned distance function grids into latent space and a latent Diffusion Transformer to denoise complete scenes, conditioned on geometry foundation models, outperforming baselines on SCRREAM and NRGB-D datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21472","ref_index":81,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Stream3D: Sequential Multi-View 3D Generation via Evidential Memory","primary_cat":"cs.CV","submitted_at":"2026-05-20T17:55:16+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21121","ref_index":67,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"ROAR-3D: Routing Arbitrary Views for High-Fidelity 3D Generation","primary_cat":"cs.CV","submitted_at":"2026-05-20T12:50:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ROAR-3D adds a token-wise view router and dual-stream attention to pretrained single-view 3D generators so they can use arbitrary unposed images for higher-fidelity output.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18365","ref_index":83,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"GeoFlow: Enforcing Implicit Geometric Consistency in Video Generation","primary_cat":"cs.CV","submitted_at":"2026-05-18T13:17:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GeoFlow adds a geometry-consistency reward based on rigid camera flow and object appearance preservation, integrated via reinforcement fine-tuning to improve geometric coherence in video generation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18132","ref_index":50,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Who Generated This 3D Asset? Learning Source Attribution for Generative 3D Models","primary_cat":"cs.CV","submitted_at":"2026-05-18T09:36:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Introduces the first passive source attribution benchmark for 22 generative 3D models and a Transformer achieving 97.22% accuracy under full supervision and 77.17% with 1% training data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.24591","ref_index":53,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Vibe Coding XR: Accelerating AI + XR Prototyping with XR Blocks and Gemini","primary_cat":"cs.HC","submitted_at":"2026-03-25T17:58:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"XR Blocks supplies an LLM-optimized Reality Model and Vibe Coding XR workflow that converts high-level prompts into working physics-aware XR applications with high one-shot success.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2601.07603","ref_index":88,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"UIKA: Fast Universal Head Avatar from Pose-Free Images","primary_cat":"cs.CV","submitted_at":"2026-01-12T14:53:56+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"UIKA is a feed-forward animatable Gaussian head model using UV-guided correspondence estimation and learnable UV tokens with dual-level attention, trained on large-scale synthetic data to handle pose-free inputs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2509.07435","ref_index":25,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"DreamLifting: A Plug-in Module Lifting MV Diffusion Models for 3D Asset Generation","primary_cat":"cs.CV","submitted_at":"2025-09-09T06:43:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"LGAA is a modular adapter framework that lifts multi-view diffusion models to produce 2D Gaussian Splats with PBR channels for high-quality relightable 3D mesh extraction using data-efficient finetuning on 69k instances.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2404.07191","ref_index":59,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models","primary_cat":"cs.CV","submitted_at":"2024-04-10T17:48:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"InstantMesh produces diverse, high-quality 3D meshes from single images in seconds by combining a multi-view diffusion model with a sparse-view large reconstruction model and optimizing directly on meshes.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"fectively map image tokens to implicit 3D triplanes with multi-view supervision. Instant3D [19] further extends LRM to sparse-view input, significantly boosting the re- construction quality. By combining with multi-view dif- fusion models, Instant3D can achieve highly generalizable and high-quality single-image to 3D generation. Inspired by Instant3D, LGM [44] and GRM [59] replace the tri- plane NeRF [29] representation with 3D Gaussians [18] to enjoy its superior rendering efficiency and circumvent the need for memory-intensive volume rendering process. However, Gaussians fall short on explicit geometry model- ing and high-quality surface extraction. Given the success of neural mesh optimization methods [39, 40], concurrent"},{"citing_arxiv_id":"2401.03890","ref_index":257,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Survey on 3D Gaussian Splatting","primary_cat":"cs.CV","submitted_at":"2024-01-08T13:42:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A survey compiling principles, applications, benchmarks, and challenges of 3D Gaussian Splatting for explicit 3D scene representation.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"Due to the discrete sampling paradigm (viewing each pixel as a single point instead of an area), 3D GS is susceptible to aliasing when dealing with varying resolutions, which leads to blurring or jagged edges. Solutions emerged at both training and inference stages. Researchers developed training-time improvements from the sampling rate perspective and introduced schemes such as multi-scale Gaussians [ 257], 2D Mip filter [276], and conditioned logistic function [128]. Inference-time solutions, such as 2D scale-adaptive filtering [207], offer enhanced fidelity that can be integrated into any existing 3D GS frameworks.ii) Reflection. Achieving realistic rendering of reflective materials is a hard, long-standing problem in 3D scene reconstruction. Recent works have introduced various approaches to model"}],"limit":50,"offset":0}