I think I would stop but the academia is so vivid about the cool things that would totally change how rendering works. I want to summrize this month as the 3D pipeline efficiency month. From creation to final image, data-driven way is changing the way. We may not need to do the math, we can just imagine the picture, the shape, in our mind.
Controable 3D scene setting, but no need for final rendering cost.
In some scientific or movie industry, we want fully control of certain objects (actors, cameras), but rendering we do not want to go through the procedure of power consuming render loop.
In this paper, the method explicitly uses G-buffer data (geometry, normals, depth, material properties) as conditioning inputs. This anchors the generative process in physically meaningful information, enabling high-fidelity results driven by actual scene structure.
Applications could include:
- Real-time graphics with enhanced quality
- Neural upscaling or denoising pipelines
- Hybrid systems where geometry informs generative outputs
FrameDiffuser: G-Buffer-Conditioned Diffusion for Neural Forward Frame Rendering
Apple seems join the world of GS in full speed!
Single image to gaussian splatting scene, that can run on standard GPU via a single feedforward pass in a NN.
- It is light-weighted.
- It is blizzing fast.
- It just need one image.
Sharp Monocular View Synthesis in Less Than a Second
TRELLIS.2 Image to 3D race is brutal.
We started our project with TRELLIS, then new compatitors joined the race and I swiched. Now I feels like that I am watching The Return of JEDI. Full PBR support, high quality of texture, eveything gets better in this loop. I just need to test some corner cases to impress myself. Great work!