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HTTP/2 200 server: GitHub.com content-type: text/html; charset=utf-8 last-modified: Tue, 11 Feb 2025 23:11:37 GMT access-control-allow-origin: * strict-transport-security: max-age=31556952 etag: W/"67abd929-65ec" expires: Sun, 28 Dec 2025 08:39:31 GMT cache-control: max-age=600 content-encoding: gzip x-proxy-cache: MISS x-github-request-id: 11C6:444BC:770B13:855572:6950EA6B accept-ranges: bytes age: 0 date: Sun, 28 Dec 2025 08:29:31 GMT via: 1.1 varnish x-served-by: cache-bom-vanm7210075-BOM x-cache: MISS x-cache-hits: 0 x-timer: S1766910571.229902,VS0,VE429 vary: Accept-Encoding x-fastly-request-id: f8067541f04ccc8502c99c89cd29159e06645194 content-length: 3860 RelitLRM: Generative Relightable Radiance for Large Reconstruction Models

RelitLRM: Generative Relightable Radiance for Large Reconstruction Models

Tianyuan Zhang ¹, Zhengfei Kuang², Haian Jin³, Zexiang Xu⁴, Sai Bi⁴, Hao Tan⁴, He Zhang⁴, Yiwei Hu⁴, Milos Hasan⁴, William T. Freeman¹, Kai Zhang^*4, Fujun Luan^*4 ^*(Equal Advising)
¹ Massachusetts Institute of Technology ²Stanford University ³Cornell University ⁴Adobe Research
ICLR, 2025 (Spotlight)

A Diffusion Transformer that tries to learn the underlying physics of reconstruction and rendering. It autonomously learns shadow casting, specular highlights, and inter-reflections, though not as precise as ray-tracing, but all neurally learned.