Abstract
Comment: 31 pages, 5 figures, and 4 tables. Code: https://github.com/sp12138/TBSM
Three-Body Scattering for Generative Modeling
- Authors
- Peng Sun, Zhenglin Cheng, Deyuan Liu...
- Categories
- cs.LG, cs.CV
Brief
Three-Body Scattering Modeling (TBSM) introduces an energy-distance based one-step generative method that replaces minibatch all-pairs critic fields with three-body interactions: each sample (projectile) is pulled toward one real target and pushed from one generated reference. The method yields a conditional expectation equal to the 2-Wasserstein gradient-flow velocity of ½DE^2(Pθ,Q). Applied with frozen image features on ImageNet-256, TBSM attains FID 2.23 (PixelDiT-XL) and 1.63 (latent DiT-XL) at NFE=1, and the authors provide a design map linking diffusion-style supervision, Drift-like dynamics, and GAN-like objectives; code is available.
Why it matters
Three-Body Scattering Modeling (TBSM) defines a constant-size per-projectile interaction: each projectile is attracted toward one real source and repelled from one independently generated source; conditioned on the projectile and its condition, its expectation equals the 2-Wasserstein gradient-flow velocity of ½·D_E^2(P_θ, Q).
Key details
- TBSM trains one-step generators on ImageNet-256 and achieves FID = 2.23 with pixel-space PixelDiT-XL and FID = 1.63 with latent-space DiT-XL at NFE = 1; training uses frozen image features, a batch of B frozen-target events gives O(B) sample-level losses (one reference per projectile), and online tracking reduces field noise versus minibatch all-pairs fields (e.g., Drifting Models).