FrameDiffuser Architecture with Dual Conditioning
Figure 2 : FrameDiffuser Architecture with dual conditioning: ControlNet processes 10-channel input comprising 9 G-buffer channels for structural guidance and 1 pred. irradiance channel for lighting guidance, computed from the previous frame’s model output and basecolor. ControlLoRA conditions on the previous frame encoded in VAE latent space for temporal coherence. The generated output at time t t is used to compute the irradiance input for the next frame at time t + 1 t+1 , enabling autoregressive frame generation. The encoder ℰ \mathcal{E} and decoder 𝒟 \mathcal{D} represent the VAE components operating in latent space. The training strategy on the right shows our three-stage approach: first, we train ControlNet on the G-buffer to image translation task without irradiance. Second, we add ControlLoRA and irradiance for temporal conditioning. Third, we train autoregressively using the model’s own generated frames as previous-frame inputs to make the model robust against its own generation errors.
Paper context
Paper title: FrameDiffuser: G-Buffer-Conditioned Diffusion for Neural Forward Frame Rendering Abstract: Neural rendering for interactive applications requires translating geometric and material properties (G-buffer) to photorealistic images with realistic lighting on a frame-by-frame basis. While recent diffusion-based approaches show promise for G-buffer-conditioned image synthesis, they face critical limitations: single-image models like RGBX generate frames independently without temporal consistency, while video models like DiffusionRenderer are too computationally expensive for most consumer gaming sets ups and require complete sequences upfront, making them unsuitable for interactive applications where future frames depend on user input. We introduce FrameDiffuser, an autoregressive neural rendering framework that generates temporally consistent, photorealistic frames by conditioning on G-buffer data and the models own previous output. After an initial frame, FrameDiffuser operates purely on incoming G-buffer data, comprising geometry, materials, and surface properties, while using its previously generated frame for temporal guidance, maintaining stable, temporal consistent generation over hundreds to thousands of frames. Our dual-conditioning architecture combines ControlNet for structural guidance with ControlLoRA for temporal coherence. A three-stage training strategy enables stable autoregressive generation. We specialize our model to individual environments, prioritizing consistency and inference speed over broad generalization, demonstrating that environment-specific Passages referencing this figure: Figure 1 : G-buffer to Photorealistic Rendering.
