CVPR 2024 Papers

Language has emerged as a natural interface for image editing. In this paper we introduce a method for region-based image editing driven by textual prompts without the need for user-provided masks or sketches. Specifically our approach leverages an existing pre-trained text-to-image model and introduces a bounding box generator to identify the editing regions that are aligned with the textual prompts. We show that this simple approach enables flexible editing that is compatible with current image generation models and is able to handle complex prompts featuring multiple objects complex sentences or lengthy paragraphs. We conduct an extensive user study to compare our method against state-of-the-art methods. The experiments demonstrate the competitive performance of our method in manipulating images with high fidelity and realism that correspond to the provided language descriptions. Our project webpage can be found at: https://yuanzelin.me/LearnableRegions_page.

This paper focuses on synthesizing high-quality and complete textures directly on the surface of 3D models within 3D space. 2D diffusion-based methods face challenges in generating 2D texture maps due to the infinite possibilities of UV mapping for a given 3D mesh. Utilizing point clouds helps circumvent variations arising from diverse mesh topologies and UV mappings. Nevertheless achieving dense point clouds to accurately represent texture details poses a challenge due to limited computational resources. To address these challenges we propose an efficient octree-based diffusion pipeline called TexOct. Our method starts by sampling a point cloud from the surface of a given 3D model with each point containing texture noise values. We utilize an octree structure to efficiently represent this point cloud. Additionally we introduce an innovative octree-based diffusion model that leverages the denoising capabilities of the Denoising Diffusion Probabilistic Model (DDPM). This model gradually reduces the texture noise on the octree nodes resulting in the restoration of fine texture. Experimental results on ShapeNet demonstrate that TexOct effectively generates high-quality 3D textures in both unconditional and text / image-conditional scenarios.

Reconstructing human-object interaction in 3D from a single RGB image is a challenging task and existing data driven methods do not generalize beyond the objects present in the carefully curated 3D interaction datasets. Capturing large-scale real data to learn strong interaction and 3D shape priors is very expensive due to the combinatorial nature of human-object interactions. In this paper we propose ProciGen (Procedural interaction Generation) a method to procedurally generate datasets with both plausible interaction and diverse object variation. We generate 1M+ human-object interaction pairs in 3D and leverage this large-scale data to train our HDM (Hierarchical Diffusion Model) a novel method to reconstruct interacting human and unseen object instances without any templates. Our HDM is an image-conditioned diffusion model that learns both realistic interaction and highly accurate human and object shapes. Experiments show that our HDM trained with ProciGen significantly outperforms prior methods that require template meshes and our dataset allows training methods with strong generalization ability to unseen object instances. Our code and data are released.

Generative models e.g. Stable Diffusion have enabled the creation of photorealistic images from text prompts. Yet the generation of 360-degree panorama images from text remains a challenge particularly due to the dearth of paired text-panorama data and the domain gap between panorama and perspective images. In this paper we introduce a novel dual-branch diffusion model named PanFusion to generate a 360-degree image from a text prompt. We leverage the stable diffusion model as one branch to provide prior knowledge in natural image generation and register it to another panorama branch for holistic image generation. We propose a unique cross-attention mechanism with projection awareness to minimize distortion during the collaborative denoising process. Our experiments validate that PanFusion surpasses existing methods and thanks to its dual-branch structure can integrate additional constraints like room layout for customized panorama outputs.

Despite the remarkable performance of score distillation in text-to-3D generation such techniques notoriously suffer from view inconsistency issues also known as "Janus" artifact where the generated objects fake each view with multiple front faces. Although empirically effective methods have approached this problem via score debiasing or prompt engineering a more rigorous perspective to explain and tackle this problem remains elusive. In this paper we reveal that the existing score distillation-based text-to-3D generation frameworks degenerate to maximal likelihood seeking on each view independently and thus suffer from the mode collapse problem manifesting as the Janus artifact in practice. To tame mode collapse we improve score distillation by re-establishing the entropy term in the corresponding variational objective which is applied to the distribution of rendered images. Maximizing the entropy encourages diversity among different views in generated 3D assets thereby mitigating the Janus problem. Based on this new objective we derive a new update rule for 3D score distillation dubbed Entropic Score Distillation (ESD). We theoretically reveal that ESD can be simplified and implemented by just adopting the classifier-free guidance trick upon variational score distillation. Although embarrassingly straightforward our extensive experiments demonstrate that ESD can be an effective treatment for Janus artifacts in score distillation.

Despite significant progress in the field it is still challenging to create personalized visual representations that align closely with the desires and preferences of individual users. This process requires users to articulate their ideas in words that are both comprehensible to the models and accurately capture their vision posing difficulties for many users. In this paper we tackle this challenge by leveraging historical user interactions with the system to enhance user prompts. We propose a novel approach that involves rewriting user prompts based on a newly collected large-scale text-to-image dataset with over 300k prompts from 3115 users. Our rewriting model enhances the expressiveness and alignment of user prompts with their intended visual outputs. Experimental results demonstrate the superiority of our methods over baseline approaches as evidenced in our new offline evaluation method and online tests. Our code and dataset are available at https://github.com/zzjchen/Tailored-Visions

Recent advances in text-to-video generation have demonstrated the utility of powerful diffusion models. Nevertheless the problem is not trivial when shaping diffusion models to animate static image (i.e. image-to-video generation). The difficulty originates from the aspect that the diffusion process of subsequent animated frames should not only preserve the faithful alignment with the given image but also pursue temporal coherence among adjacent frames. To alleviate this we present TRIP a new recipe of image-to-video diffusion paradigm that pivots on image noise prior derived from static image to jointly trigger inter-frame relational reasoning and ease the coherent temporal modeling via temporal residual learning. Technically the image noise prior is first attained through one-step backward diffusion process based on both static image and noised video latent codes. Next TRIP executes a residual-like dual-path scheme for noise prediction: 1) a shortcut path that directly takes image noise prior as the reference noise of each frame to amplify the alignment between the first frame and subsequent frames; 2) a residual path that employs 3D-UNet over noised video and static image latent codes to enable inter-frame relational reasoning thereby easing the learning of the residual noise for each frame. Furthermore both reference and residual noise of each frame are dynamically merged via attention mechanism for final video generation. Extensive experiments on WebVid-10M DTDB and MSR-VTT datasets demonstrate the effectiveness of our TRIP for image-to-video generation. Please see our project page at https://trip-i2v.github.io/TRIP/.

Large multimodal language models have shown remarkable proficiency in understanding and editing images. However a majority of these visually-tuned models struggle to comprehend the textual content embedded in images primarily due to the limitation of training data. In this work we introduce TRINS: a Text-Rich image1 INStruction dataset with the objective of enhancing the reading ability of the multimodal large language model. TRINS is built upon LAION 2 using hybrid data annotation strategies that include machine-assisted and human-assisted annotation process. It contains 39153 text-rich images captions and 102437 questions. Specifically we show that the number of words per annotation in TRINS is significantly longer than that of related datasets providing new challenges. Furthermore we introduce a simple and effective architecture called a Language-Vision Reading Assistant (LaRA) which is good at understanding textual content within images. LaRA outperforms existing state-of-the-art multimodal large language models on the TRINS dataset as well as other classical benchmarks. Lastly we conducted a comprehensive evaluation with TRINS on various text-rich image understanding and generation tasks demonstrating its effectiveness.

Despite their ability to generate high-resolution and diverse images from text prompts text-to-image diffusion models often suffer from slow iterative sampling processes. Model distillation is one of the most effective directions to accelerate these models. However previous distillation methods fail to retain the generation quality while requiring a significant amount of images for training either from real data or synthetically generated by the teacher model. In response to this limitation we present a novel image-free distillation scheme named SwiftBrush. Drawing inspiration from text-to-3D synthesis in which a 3D neural radiance field that aligns with the input prompt can be obtained from a 2D text-to-image diffusion prior via a specialized loss without the use of any 3D data ground-truth our approach re-purposes that same loss for distilling a pretrained multi-step text-to-image model to a student network that can generate high-fidelity images with just a single inference step. In spite of its simplicity our model stands as one of the first one-step text-to-image generators that can produce images of comparable quality to Stable Diffusion without reliance on any training image data. Remarkably SwiftBrush achieves an FID score of 16.67 and a CLIP score of 0.29 on the COCO-30K benchmark achieving competitive results or even substantially surpassing existing state-of-the-art distillation techniques.

We propose a method that can generate cinemagraphs automatically from a still landscape image using a pre-trained StyleGAN. Inspired by the success of recent unconditional video generation we leverage a powerful pre-trained image generator to synthesize high-quality cinemagraphs. Unlike previous approaches that mainly utilize the latent space of a pre-trained StyleGAN our approach utilizes its deep feature space for both GAN inversion and cinemagraph generation. Specifically we propose multi-scale deep feature warping (MSDFW) which warps the intermediate features of a pre-trained StyleGAN at different resolutions. By using MSDFW the generated cinemagraphs are of high resolution and exhibit plausible looping animation. We demonstrate the superiority of our method through user studies and quantitative comparisons with state-of-the-art cinemagraph generation methods and a video generation method that uses a pre-trained StyleGAN.

Large-scale Text-to-Image (T2I) models have rapidly gained prominence across creative fields generating visually compelling outputs from textual prompts. However controlling these models to ensure consistent style remains challenging with existing methods necessitating fine-tuning and manual intervention to disentangle content and style. In this paper we introduce StyleAligned a novel technique designed to establish style alignment among a series of generated images. By employing minimal `attention sharing' during the diffusion process our method maintains style consistency across images within T2I models. This approach allows for the creation of style-consistent images using a reference style through a straightforward inversion operation. Our method's evaluation across diverse styles and text prompts demonstrates high-quality synthesis and fidelity underscoring its efficacy in achieving consistent style across various inputs.

We describe a novel method StyLitGAN for relighting and resurfacing images in the absence of labeled data. StyLitGAN generates images with realistic lighting effects including cast shadows soft shadows inter-reflections and glossy effects without the need for paired or CGI data. StyLitGAN uses an intrinsic image method to decompose an image followed by a search of the latent space of a pretrained StyleGAN to identify a set of directions. By prompting the model to fix one component (e.g. albedo) and vary another (e.g. shading) we generate relighted images by adding the identified directions to the latent style codes. Quantitative metrics of change in albedo and lighting diversity allow us to choose effective directions using a forward selection process. Qualitative evaluation confirms the effectiveness of our method.

Diffusion models have demonstrated exceptional efficacy in various generative applications. While existing models focus on minimizing a weighted sum of denoising score matching losses for data distribution modeling their training primarily emphasizes instance-level optimization overlooking valuable structural information within each mini-batch indicative of pair-wise relationships among samples. To address this limitation we introduce Structure-guided Adversarial training of Diffusion Models (SADM). In this pioneering approach we compel the model to learn manifold structures between samples in each training batch. To ensure the model captures authentic manifold structures in the data distribution we advocate adversarial training of the diffusion generator against a novel structure discriminator in a minimax game distinguishing real manifold structures from the generated ones. SADM substantially outperforms existing methods in image generation and cross-domain fine-tuning tasks across 12 datasets establishing a new state-of-the-art FID of 1.58 and 2.11 on ImageNet for class-conditional image generation at resolutions of 256x256 and 512x512 respectively.

Current 3D-aware facial NeRF generation approaches control the facial appearance by text lighting conditions or reference images limiting precise manipulation of local facial regions and interactivity. Color stroke a user-friendly and effective tool to depict appearance is challenging to edit 3D faces because of the lack of texture coarse geometry representation and detailed editing operations. To solve the above problems we introduce StrokeFaceNeRF a novel stroke-based method for editing facial NeRF appearance. In order to infer the missing texture and 3D geometry information 2D edited stroke maps are firstly encoded into the EG3D's latent space followed by a transformer-based editing module to achieve effective appearance changes while preserving the original geometry in editing regions. Notably we design a novel geometry loss function to ensure surface density remains consistent during training. To further enhance the local manipulation accuracy we propose a stereo fusion approach which lifts the 2D mask (inferred from strokes or drawn by users) into 3D mask volume allowing explicit blending of the original and edited faces. Extensive experiments validate that the proposed method outperforms existing 2D and 3D methods in both editing reality and geometry retention.

Estimating 3D full-body avatars from AR/VR devices is essential for creating immersive experiences in AR/VR applications. This task is challenging due to the limited input from Head Mounted Devices which capture only sparse observations from the head and hands. Predicting the full-body avatars particularly the lower body from these sparse observations presents significant difficulties. In this paper we are inspired by the inherent property of the kinematic tree defined in the Skinned Multi-Person Linear (SMPL) model where the upper body and lower body share only one common ancestor node bringing the potential of decoupled reconstruction. We propose a stratified approach to decouple the conventional full-body avatar reconstruction pipeline into two stages with the reconstruction of the upper body first and a subsequent reconstruction of the lower body conditioned on the previous stage. To implement this straightforward idea we leverage the latent diffusion model as a powerful probabilistic generator and train it to follow the latent distribution of decoupled motions explored by a VQ-VAE encoder-decoder model. Extensive experiments on AMASS mocap dataset demonstrate our state-of-the-art performance in the reconstruction of full-body motions.

Most image-to-image translation models postulate that a unique correspondence exists between the semantic classes of the source and target domains. However this assumption does not always hold in real-world scenarios due to divergent distributions different class sets and asymmetrical information representation. As conventional GANs attempt to generate images that match the distribution of the target domain they may hallucinate spurious instances of classes absent from the source domain thereby diminishing the usefulness and reliability of translated images. CycleGAN-based methods are also known to hide the mismatched information in the generated images to bypass cycle consistency objectives a process known as steganography. In response to the challenge of non-bijective image translation we introduce StegoGAN a novel model that leverages steganography to prevent spurious features in generated images. Our approach enhances the semantic consistency of the translated images without requiring additional postprocessing or supervision. Our experimental evaluations demonstrate that StegoGAN outperforms existing GAN-based models across various non-bijective image-to-image translation tasks both qualitatively and quantitatively. Our code and pretrained models are accessible at https://github.com/sian-wusidi/StegoGAN.

We introduce the Splatter Image an ultra-efficient approach for monocular 3D object reconstruction. Splatter Image is based on Gaussian Splatting which allows fast and high-quality reconstruction of 3D scenes from multiple images. We apply Gaussian Splatting to monocular reconstruction by learning a neural network that at test time performs reconstruction in a feed-forward manner at 38 FPS. Our main innovation is the surprisingly straightforward design of this network which using 2D operators maps the input image to one 3D Gaussian per pixel. The resulting set of Gaussians thus has the form an image the Splatter Image. We further extend the method take several images as input via cross-view attention. Owning to the speed of the renderer (588 FPS) we use a single GPU for training while generating entire images at each iteration to optimize perceptual metrics like LPIPS. On several synthetic real multi-category and large-scale benchmark datasets we achieve better results in terms of PSNR LPIPS and other metrics while training and evaluating much faster than prior works. Code models and more results are available at https://szymanowiczs.github.io/ splatter-image.