CVPR 2024 论文列表

We introduce a new task of generating "Illustrated Instructions" i.e. visual instructions customized to a user's needs. We identify desiderata unique to this task and formalize it through a suite of automatic and human evaluation metrics designed to measure the validity consistency and efficacy of the generations. We combine the power of large language models (LLMs) together with strong text-to-image generation diffusion models to propose a simple approach called StackedDiffusion which generates such illustrated instructions given text as input. The resulting model strongly outperforms baseline approaches and state-of-the-art multimodal LLMs; and in 30% of cases users even prefer it to human-generated articles. Most notably it enables various new and exciting applications far beyond what static articles on the web can provide such as personalized instructions complete with intermediate steps and pictures in response to a user's individual situation.

Few-shot font generation (FFG) produces stylized font images with a limited number of reference samples which can significantly reduce labor costs in manual font designs. Most existing FFG methods follow the style-content disentanglement paradigm and employ the Generative Adversarial Network (GAN) to generate target fonts by combining the decoupled content and style representations. The complicated structure and detailed style are simultaneously generated in those methods which may be the sub-optimal solutions for FFG task. Inspired by most manual font design processes of expert designers in this paper we model font generation as a multi-stage generative process. Specifically as the injected noise and the data distribution in diffusion models can be well-separated into different sub-spaces we are able to incorporate the font transfer process into these models. Based on this observation we generalize diffusion methods to model font generative process by separating the reverse diffusion process into three stages with different functions: The structure construction stage first generates the structure information for the target character based on the source image and the font transfer stage subsequently transforms the source font to the target font. Finally the font refinement stage enhances the appearances and local details of the target font images. Based on the above multi-stage generative process we construct our font generation framework named MSD-Font with a dual-network approach to generate font images. The superior performance demonstrates the effectiveness of our model. The code is available at: https://github.com/fubinfb/MSD-Font .

Whole Slide Image (WSI) classification is often formulated as a Multiple Instance Learning (MIL) problem. Recently Vision-Language Models (VLMs) have demonstrated remarkable performance in WSI classification. However existing methods leverage coarse-grained pathogenetic descriptions for visual representation supervision which are insufficient to capture the complex visual appearance of pathogenetic images hindering the generalizability of models on diverse downstream tasks. Additionally processing high-resolution WSIs can be computationally expensive. In this paper we propose a novel "Fine-grained Visual-Semantic Interaction" (FiVE) framework for WSI classification. It is designed to enhance the model's generalizability by leveraging the interaction between localized visual patterns and fine-grained pathological semantics. Specifically with meticulously designed queries we start by utilizing a large language model to extract fine-grained pathological descriptions from various non-standardized raw reports. The output descriptions are then reconstructed into fine-grained labels used for training. By introducing a Task-specific Fine-grained Semantics (TFS) module we enable prompts to capture crucial visual information in WSIs which enhances representation learning and augments generalization capabilities significantly. Furthermore given that pathological visual patterns are redundantly distributed across tissue slices we sample a subset of visual instances during training. Our method demonstrates robust generalizability and strong transferability dominantly outperforming the counterparts on the TCGA Lung Cancer dataset with at least 9.19% higher accuracy in few-shot experiments. The code is available at: https://github.com/ls1rius/WSI_FiVE.

Recently we have witnessed the explosive growth of various volumetric representations in modeling animatable head avatars. However due to the diversity of frameworks there is no practical method to support high-level applications like 3D head avatar editing across different representations. In this paper we propose a generic avatar editing approach that can be universally applied to various 3DMM driving volumetric head avatars. To achieve this goal we design a novel expression-aware modification generative model which enables lift 2D editing from a single image to a consistent 3D modification field. To ensure the effectiveness of the generative modification process we develop several techniques including an expression-dependent modification distillation scheme to draw knowledge from the large-scale head avatar model and 2D facial texture editing tools implicit latent space guidance to enhance model convergence and a segmentation-based loss reweight strategy for fine-grained texture inversion. Extensive experiments demonstrate that our method delivers high-quality and consistent results across multiple expression and viewpoints. Project page: https://zju3dv.github.io/ geneavatar/.

In this study we explore Transformer based diffusion models for image and video generation. Despite the dominance of Transformer architectures in various fields due to their flexibility and scalability the visual generative domain primarily utilizes CNN-based U-Net architectures particularly in diffusion-based models. We introduce GenTron a family of Generative models employing Transformer-based diffusion to address this gap. Our initial step was to adapt Diffusion Transformers (DiTs) from class to text conditioning a process involving thorough empirical exploration of the conditioning mechanism. We then scale GenTron from approximately 900M to over 3B parameters observing improvements in visual quality. Furthermore we extend GenTron to text-to-video generation incorporating novel motion-free guidance to enhance video quality. In human evaluations against SDXL GenTron achieves a 51.1% win rate in visual quality (with a 19.8% draw rate) and a 42.3% win rate in text alignment (with a 42.9% draw rate). GenTron notably performs well in T2I-CompBench highlighting its compositional generation ability. We hope GenTron could provide meaningful insights and serve as a valuable reference for future research. Please refer to the arXiv version for the most up-to-date results: https://arxiv.org/abs/2312.04557.

In recent times the generation of 3D assets from text prompts has shown impressive results. Both 2D and 3D diffusion models can help generate decent 3D objects based on prompts. 3D diffusion models have good 3D consistency but their quality and generalization are limited as trainable 3D data is expensive and hard to obtain. 2D diffusion models enjoy strong abilities of generalization and fine generation but 3D consistency is hard to guarantee. This paper attempts to bridge the power from the two types of diffusion models via the recent explicit and efficient 3D Gaussian splatting representation. A fast 3D object generation framework named as GaussianDreamer is proposed where the 3D diffusion model provides priors for initialization and the 2D diffusion model enriches the geometry and appearance. Operations of noisy point growing and color perturbation are introduced to enhance the initialized Gaussians. Our GaussianDreamer can generate a high-quality 3D instance or 3D avatar within 15 minutes on one GPU much faster than previous methods while the generated instances can be directly rendered in real time. Demos and code are available at https://taoranyi.com/gaussiandreamer/.

Efficient generation of 3D digital humans is important in several industries including virtual reality social media and cinematic production. 3D generative adversarial networks (GANs) have demonstrated state-of-the-art (SOTA) quality and diversity for generated assets. Current 3D GAN architectures however typically rely on volume representations which are slow to render thereby hampering the GAN training and requiring multi-view-inconsistent 2D upsamplers. Here we introduce Gaussian Shell Maps (GSMs) as a framework that connects SOTA generator network architectures with emerging 3D Gaussian rendering primitives using an articulable multi shell--based scaffold. In this setting a CNN generates a 3D texture stack with features that are mapped to the shells. The latter represent inflated and deflated versions of a template surface of a digital human in a canonical body pose. Instead of rasterizing the shells directly we sample 3D Gaussians on the shells whose attributes are encoded in the texture features. These Gaussians are efficiently and differentiably rendered. The ability to articulate the shells is important during GAN training and at inference time to deform a body into arbitrary user-defined poses. Our efficient rendering scheme bypasses the need for view-inconsistent upsamplers and achieves high-quality multi-view consistent renderings at a native resolution of 512 x512 pixels. We demonstrate that GSMs successfully generate 3D humans when trained on single-view datasets including SHHQ and DeepFashion.

Ethical concerns surrounding copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models. One effective solution involves watermarking the generated images. However existing methods often compromise the model performance or require additional training which is undesirable for operators and users. To address this issue we propose Gaussian Shading a diffusion model watermarking technique that is both performance-lossless and training-free while serving the dual purpose of copyright protection and tracing of offending content. Our watermark embedding is free of model parameter modifications and thus is plug-and-play. We map the watermark to latent representations following a standard Gaussian distribution which is indistinguishable from latent representations obtained from the non-watermarked diffusion model. Therefore we can achieve watermark embedding with lossless performance for which we also provide theoretical proof. Furthermore since the watermark is intricately linked with image semantics it exhibits resilience to lossy processing and erasure attempts. The watermark can be extracted by Denoising Diffusion Implicit Models (DDIM) inversion and inverse sampling. We evaluate Gaussian Shading on multiple versions of Stable Diffusion and the results demonstrate that Gaussian Shading not only is performance-lossless but also outperforms existing methods in terms of robustness.

Multimodal Large Language Models (MLLMs) have excelled in 2D image-text comprehension and image generation but their understanding of the 3D world is notably deficient limiting progress in 3D language understanding and generation. To solve this problem we introduce GPT4Point an innovative groundbreaking point-language multimodal model designed specifically for unified 3D object understanding and generation within the MLLM framework. GPT4Point as a powerful 3D MLLM seamlessly can execute a variety of point-text reference tasks such as point-cloud captioning and Q&A. Additionally GPT4Point is equipped with advanced capabilities for controllable 3D generation it can get high-quality results through a low-quality point-text feature maintaining the geometric shapes and colors. To support the expansive needs of 3D object-text pairs we develop Pyramid-XL a point-language dataset annotation engine. It constructs a large-scale database over 1M objects of varied text granularity levels from the Objaverse-XL dataset essential for training GPT4Point. A comprehensive benchmark has been proposed to evaluate 3D point-language understanding capabilities. In extensive evaluations GPT4Point has demonstrated superior performance in understanding and generation.

Despite recent advances in text-to-3D generative methods there is a notable absence of reliable evaluation metrics. Existing metrics usually focus on a single criterion each such as how well the asset aligned with the input text. These metrics lack the flexibility to generalize to different evaluation criteria and might not align well with human preferences. Conducting user preference studies is an alternative that offers both adaptability and human-aligned results. User studies however can be very expensive to scale. This paper presents an automatic versatile and human-aligned evaluation metric for text-to-3D generative models. To this end we first develop a prompt generator using GPT-4V to generate evaluating prompts which serve as input to compare text-to-3D models. We further design a method instructing GPT-4V to compare two 3D assets according to user-defined criteria. Finally we use these pairwise comparison results to assign these models Elo ratings. Experimental results suggest our metric strongly align with human preference across different evaluation criteria.

Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations. In this paper we seek to leverage Gaussian splatting to generate realistic animatable avatars from textual descriptions addressing the limitations (e.g. efficiency and flexibility) imposed by mesh or NeRF-based representations. However a naive application of Gaussian splatting cannot generate high-quality animatable avatars and suffers from learning instability; it also cannot capture fine avatar geometries and often leads to degenerate body parts. To tackle these problems we first propose a primitive-based 3D Gaussian representation where Gaussians are defined inside pose-driven primitives to facilitate animations. Second to stabilize and amortize the learning of millions of Gaussians we propose to use implicit neural fields to predict the Gaussian attributes (e.g. colors). Finally to capture fine avatar geometries and extract detailed meshes we propose a novel SDF-based implicit mesh learning approach for 3D Gaussians that regularizes the underlying geometries and extracts highly detailed textured meshes. Our proposed method GAvatar enables the large-scale generation of diverse animatable avatars using only text prompts. GAvatar significantly surpasses existing methods in terms of both appearance and geometry quality and achieves extremely fast rendering (100 fps) at 1K resolution.

Novel view synthesis aims to generate new view images of a given view image collection. Recent attempts address this problem relying on 3D geometry priors (e.g. shapes sizes and positions) learned from multi-view images. However such methods encounter the following limitations: 1) they require a set of multi-view images as training data for a specific scene (e.g. face car or chair) which is often unavailable in many real-world scenarios; 2) they fail to extract the geometry priors from single-view images due to the lack of multi-view supervision. In this paper we propose a Geometry-enhanced NeRF (G-NeRF) which seeks to enhance the geometry priors by a geometry-guided multi-view synthesis approach followed by a depth-aware training. In the synthesis process inspired that existing 3D GAN models can unconditionally synthesize high-fidelity multi-view images we seek to adopt off-the-shelf 3D GAN models such as EG3D as a free source to provide geometry priors through synthesizing multi-view data. Simultaneously to further improve the geometry quality of the synthetic data we introduce a truncation method to effectively sample latent codes within 3D GAN models. To tackle the absence of multi-view supervision for single-view images we design the depth-aware training approach incorporating a depth-aware discriminator to guide geometry priors through depth maps. Experiments demonstrate the effectiveness of our method in terms of both qualitative and quantitative results.

In this paper we uncover the untapped potential of diffusion U-Net which serves as a "free lunch" that substantially improves the generation quality on the fly. We initially investigate the key contributions of the U-Net architecture to the denoising process and identify that its main backbone primarily contributes to denoising whereas its skip connections mainly introduce high-frequency features into the decoder module causing the potential neglect of crucial functions intrinsic to the backbone network. Capitalizing on this discovery we propose a simple yet effective method termed "FreeU" which enhances generation quality without additional training or finetuning. Our key insight is to strategically re-weight the contributions sourced from the U-Net's skip connections and backbone feature maps to leverage the strengths of both components of the U-Net architecture. Promising results on image and video generation tasks demonstrate that our FreeU can be readily integrated to existing diffusion models e.g. Stable Diffusion DreamBooth and ControlNet to improve the generation quality with only a few lines of code. All you need is to adjust two scaling factors during inference.

To serve the intricate and varied demands of image editing precise and flexible manipulation in image content is indispensable. Recently Drag-based editing methods have gained impressive performance. However these methods predominantly center on point dragging resulting in two noteworthy drawbacks namely "miss tracking" where difficulties arise in accurately tracking the predetermined handle points and "ambiguous tracking" where tracked points are potentially positioned in wrong regions that closely resemble the handle points. To address the above issues we propose FreeDrag a feature dragging methodology designed to free the burden on point tracking. The FreeDrag incorporates two key designs i.e. template feature via adaptive updating and line search with backtracking the former improves the stability against drastic content change by elaborately controlling the feature updating scale after each dragging while the latter alleviates the misguidance from similar points by actively restricting the search area in a line. These two technologies together contribute to a more stable semantic dragging with higher efficiency. Comprehensive experimental results substantiate that our approach significantly outperforms pre-existing methodologies offering reliable point-based editing even in various complex scenarios.

Benefiting from large-scale pre-trained text-to-image (T2I) generative models impressive progress has been achieved in customized image generation which aims to generate user-specified concepts. Existing approaches have extensively focused on single-concept customization and still encounter challenges when it comes to complex scenarios that involve combining multiple concepts. These approaches often require retraining/fine-tuning using a few images leading to time-consuming training processes and impeding their swift implementation. Furthermore the reliance on multiple images to represent a singular concept increases the difficulty of customization. To this end we propose FreeCustom a novel tuning-free method to generate customized images of multi-concept composition based on reference concepts using only one image per concept as input. Specifically we introduce a new multi-reference self-attention (MRSA) mechanism and a weighted mask strategy that enables the generated image to access and focus more on the reference concepts. In addition MRSA leverages our key finding that input concepts are better preserved when providing images with context interactions. Experiments show that our method's produced images are consistent with the given concepts and better aligned with the input text. Our method outperforms or performs on par with other training-based methods in terms of multi-concept composition and single-concept customization but is simpler. Codes can be found \href https://github.com/aim-uofa/FreeCustom here .

Recent approaches such as ControlNet offer users fine-grained spatial control over text-to-image (T2I) diffusion models. However auxiliary modules have to be trained for each spatial condition type model architecture and checkpoint putting them at odds with the diverse intents and preferences a human designer would like to convey to the AI models during the content creation process. In this work we present FreeControl a training-free approach for controllable T2I generation that supports multiple conditions architectures and checkpoints simultaneously. FreeControl enforces structure guidance to facilitate the global alignment with a guidance image and appearance guidance to collect visual details from images generated without control. Extensive qualitative and quantitative experiments demonstrate the superior performance of FreeControl across a variety of pre-trained T2I models. In particular FreeControl enables convenient training-free control over many different architectures and checkpoints allows the challenging input conditions on which most of the existing training-free methods fail and achieves competitive synthesis quality compared to training-based approaches. Project page:https://genforce.github.io/freecontrol/.

We introduce Free3D a simple accurate method for monocular open-set novel view synthesis (NVS). Similar to Zero-1-to-3 we start from a pre-trained 2D image generator for generalization and fine-tune it for NVS. Compared to other works that took a similar approach we obtain significant improvements without resorting to an explicit 3D representation which is slow and memory-consuming and without training an additional network for 3D reconstruction. Our key contribution is to improve the way the target camera pose is encoded in the network which we do by introducing a new ray conditioning normalization (RCN) layer. The latter injects pose information in the underlying 2D image generator by telling each pixel its viewing direction. We further improve multi-view consistency by using light-weight multi-view attention layers and by sharing generation noise between the different views. We train Free3D on the Objaverse dataset and demonstrate excellent generalization to new categories in new datasets including OmniObject3D and GSO. The project page is available at https://chuanxiaz.com/free3d/.

3D Gaussian splatting has achieved very impressive performance in real-time novel view synthesis. However it often suffers from over-reconstruction during Gaussian densification where high-variance image regions are covered by a few large Gaussians only leading to blur and artifacts in the rendered images. We design a progressive frequency regularization (FreGS) technique to tackle the over-reconstruction issue within the frequency space. Specifically FreGS performs coarse-to-fine Gaussian densification by exploiting low-to-high frequency components that can be easily extracted with low-pass and high-pass filters in the Fourier space. By minimizing the discrepancy between the frequency spectrum of the rendered image and the corresponding ground truth it achieves high-quality Gaussian densification and alleviates the over-reconstruction of Gaussian splatting effectively. Experiments over multiple widely adopted benchmarks (e.g. Mip-NeRF360 Tanks-and-Temples and Deep Blending) show that FreGS achieves superior novel view synthesis and outperforms the state-of-the-art consistently.

Computer vision models normally witness degraded performance when deployed in real-world scenarios due to unexpected changes in inputs that were not accounted for during training. Data augmentation is commonly used to address this issue as it aims to increase data variety and reduce the distribution gap between training and test data. However common visual augmentations might not guarantee extensive robustness of computer vision models. In this paper we propose Auxiliary Fourier-basis Augmentation (AFA) a complementary technique targeting augmentation in the frequency domain and filling the robustness gap left by visual augmentations. We demonstrate the utility of augmentation via Fourier-basis additive noise in a straightforward and efficient adversarial setting. Our results show that AFA benefits the robustness of models against common corruptions OOD generalization and consistency of performance of models against increasing perturbations with negligible deficit to the standard performance of models. It can be seamlessly integrated with other augmentation techniques to further boost performance. Codes and models are available at \href https://github.com/nis-research/afa-augment https://github.com/nis-research/afa-augment .