[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82645-en":3,"doc-seo-82645-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82645,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","PixGS: Pixel-Space Diffusion for Direct 3D Gaussian Splat Generation","Recent advances in 3D content generation from text or images are limited by view inconsistency from 2D generators and the scarcity of high-quality 3D data. Prior approaches often adapt latent diffusion pipelines to produce 3D Gaussian splats, but cascaded training is computationally costly and constrained by lossy latent representations, causing decoding artifacts and accumulated errors. PixGS presents a single-stage, pixel-space diffusion pipeline that denoises 3D Gaussian attributes directly, with depth, normal, and high-frequency supervision to improve geometry and appearance while retaining fast inference.","arXiv :2607 .0 1803v2 [ cs .CV] 4 Jul 2026  \nPixGS: Pixel-Space Diffusion for Direct 3D Gaussian Splat Generation  \nCao Duy Phong Nguyen  \nQualcomm AI Research⋆  \n{duycao, phongnh}@qti.qualcomm.com  \nAbstract. Recent advances in 3D content generation from text or images have achieved impressive results, yet view inconsistency from 2D generators and the scarcity of high-quality 3D data remain significant bottlenecks. Existing solutions [22, 29] typically adapt large-scale pretrained text-to-image latent diffusion models to generate 3D Gaussian Splats (3DGS) . However, these approaches often rely on training complex cascade pipelines that are computationally expensive and scalabilitylimited. Most critically, the quality of generated 3D assets is inherently constrained by each component capacity and compressed latent space, leading to decoding artifacts and accumulated errors. To address these limitations, we propose PixGS, a single-stage pipeline for direct highquality 3DGS generation, which leverages recent advances in pixel-space diffusion to bypass lossy latent compression while still benefiting from the vast 2D generative priors. By directly denoising 3D Gaussian attributes at each timestep, our method enables precise, splat-level regularization of both appearance and geometry. Furthermore, we introduce a comprehensive supervision strategy that incorporates surface normals, depth, and high-frequency structural information, which is often overlooked in prior works. Experiments demonstrate that PixGS outperforms current state-of-the-art methods while maintaining a fast inference speed (≈ 1son a single A100 GPU), offering a robust and efficient alternative to multi-stage generation pipelines.  \nKeywords: 3D Generation · Gaussian Splatting · Generative Model  \n1 Introduction  \nWhile recent 3D generative advances often favor mesh-based representations [13, 19,45, 60] for their geometric clarity in animation and printing, domains such as VR/AR, gaming and film prioritize visual realism and rendering efficiency. This has cemented 3D Gaussian Splats (3DGS) [17, 18] as a pivotal representation, fueling research efforts to integrate its superior rendering capabilities into robust generative pipelines.  \nSeveral existing methods [42,48,51] attempt to synthesize 3DGS using multiview-aware 2D diffusion models [24, 38] to generate consistent images of an object, followed by a separate 3D reconstruction stage. While promising, the final  \n⋆ Qualcomm AI Research is an initiative of Qualcomm Technologies, Inc.  \n2 C. Duy and P. Nguyen  \n3D quality is strictly capped by the upstream 2D generator, and any slight inconsistency in the generated views leads to artifacts (floaters) during reconstruction.  \nA recent promising direction involves directly generating 3DGS in an imagelike format. Inspired by generalizable 3DGS reconstruction techniques [3, 41], DiffSplat [22] finetunes latent diffusion models to generate 2D tensors where each pixel encodes the attributes of a 3D Gaussian. While this provides multiview consistency and leverages 2D priors, it introduces a significant architectural bottleneck. The pipeline requires training three interdependent models: a 3DGS reconstructor, a VAE to compress attributes into a latent space, and a latent diffusion model. This cascaded training regime is computationally expensive and introduces substantial engineering overhead, as each stage must converge reasonably well before the next can proceed, making the overall system fragile and difficult to scale. Also, to mitigate the computation costs of repeated Gaussian decoding during diffusion training, DiffSplat employs an additional lightweight decoder, trained from scratch, which reduces memory usage at the expense of increased system complexity. Moreover, reliance on a compressed latent space inherently limits the representational capacity and fine-grained detail of the generated 3DGS.  \nWe propose PixGS, a single-stage pipeline for high-quality 3DGS gener","cbCaicruNDRejUsG","https://ap.wps.com/l/cbCaicruNDRejUsG","pdf",35778514,1,27,"English","en",105,"# Introduction\n## Background and Challenges\n## Related Work: Latent and Image-Like 3DGS Generation\n## Proposed Method: PixGS\n### Contributions","[{\"question\":\"What problem does PixGS address in 3D Gaussian splat generation?\",\"answer\":\"PixGS targets view inconsistency caused by 2D generators and the bottlenecks of prior latent, multi-stage pipelines that rely on lossy compression and complex cascade training.\"},{\"question\":\"How is PixGS different from DiffSplat and other latent approaches?\",\"answer\":\"PixGS uses a single-stage pixel-space diffusion process that directly denoises 3D Gaussian attribute tensors, avoiding VAE latent compression and reducing architectural and engineering fragility.\"},{\"question\":\"What supervision signals does PixGS use to improve 3D quality?\",\"answer\":\"PixGS incorporates surface normals, depth, and a Laplacian of Gaussian (LoG) loss to regularize high-frequency structural details that earlier methods often overlook.\"}]",1784182037,68,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"pixgs-pixel-space-diffusion-for-direct-3d-gaussian-splat-generation","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/pixgs-pixel-space-diffusion-for-direct-3d-gaussian-splat-generation/82645/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does PixGS address in 3D Gaussian splat generation?","Question",{"text":75,"@type":76},"PixGS targets view inconsistency caused by 2D generators and the bottlenecks of prior latent, multi-stage pipelines that rely on lossy compression and complex cascade training.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is PixGS different from DiffSplat and other latent approaches?",{"text":80,"@type":76},"PixGS uses a single-stage pixel-space diffusion process that directly denoises 3D Gaussian attribute tensors, avoiding VAE latent compression and reducing architectural and engineering fragility.",{"name":82,"@type":73,"acceptedAnswer":83},"What supervision signals does PixGS use to improve 3D quality?",{"text":84,"@type":76},"PixGS incorporates surface normals, depth, and a Laplacian of Gaussian (LoG) loss to regularize high-frequency structural details that earlier methods often 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