DiLightNet: Fine-grained Lighting Control for Diffusion-based Image Generation

This paper presents a novel method for exerting fine-grained lighting control during text-driven diffusion-based image generation. While existing diffusion models already have the ability to generate images under any lighting condition, without additional guidance these models tend to correlate image content and lighting. Moreover, text prompts lack the necessary expressional power to describe detailed lighting setups. To provide the content creator with fine-grained control over the lighting during image generation, we augment the text-prompt with detailed lighting information in the form of radiance hints, i.e., visualizations of the scene geometry with a homogeneous canonical material under the target lighting. However, the scene geometry needed to produce the radiance hints is unknown. Our key observation is that we only need to guide the diffusion process, hence exact radiance hints are not necessary; we only need to point the diffusion model in the right direction. Based on this observation, we introduce a three stage method for controlling the lighting during image generation. In the first stage, we leverage a standard pretrained diffusion model to generate a provisional image under uncontrolled lighting. Next, in the second stage, we resynthesize and refine the foreground object in the generated image by passing the target lighting to a refined diffusion model, named DiLightNet, using radiance hints computed on a coarse shape of the foreground object inferred from the provisional image. To retain the texture details, we multiply the radiance hints with a neural encoding of the provisional synthesized image before passing it to DiLightNet. Finally, in the third stage, we resynthesize the background to be consistent with the lighting on the foreground object. We demonstrate and validate our lighting controlled diffusion model on a variety of text prompts and lighting conditions.

Learning from Multi-View Representation for Point-Cloud Pre-Training

A critical problem in the pre-training of 3D point clouds is leveraging massive 2D data. A fundamental challenge is to address the 2D-3D domain gap. This paper proposes a novel approach to point-cloud pre-training that enables learning 3D representations by leveraging pre-trained 2D-based networks. In particular, it avoids overfitting to 2D representations and potentially discarding critical 3D features for 3D recognition tasks. The key to our approach is a novel multi-view representation, which learns a shared 3D feature volume consistent with deep features extracted from multiple 2D camera views. The 2D deep features are regularized using pre-trained 2D networks through the 2D knowledge transfer loss. To prevent the resulting 3D feature representations from discarding 3D signals, we introduce the multi-view consistency loss that forces the projected 2D feature representations to capture pixel-wise correspondences across different views. Such correspondences induce 3D geometry and effectively retain 3D features in the projected 2D features. Experimental results demonstrate that our pre-trained model can be successfully transferred to various downstream tasks, including 3D detection and semantic segmentation, and achieve state-of-the-art performance.