CVPR 2024 Papers

With the emergence of AR/VR 3D models are in tremendous demand. However conventional 3D modeling with Computer-Aided Design software requires much expertise and is difficult for novice users. We find that AR/VR devices in addition to serving as effective display mediums can offer a promising potential as an intuitive 3D model creation tool especially with the assistance of AI generative models. Here we propose Deep3DVRSketch the first 3D model generation network that inputs 3D VR sketches from novice users and generates highly consistent 3D models in multiple categories within seconds irrespective of the users' drawing abilities. We also contribute KO3D+ the largest 3D sketch-shape dataset. Our method pre-trains a conditional diffusion model on quality 3D data then fine-tunes an encoder to map 3D sketches onto the generator's manifold using an adaptive curriculum strategy for limited ground truths. In our experiment our approach achieves state-of-the-art performance in both model quality and fidelity with real-world input from novice users and users can even draw and obtain very detailed geometric structures. In our user study users were able to complete the 3D modeling tasks over 10 times faster using our approach compared to conventional CAD software tools. We believe that our Deep3DVRSketch and KO3D+ dataset can offer a promising solution for future 3D modeling in metaverse era. Check the project page at http://research.kokoni3d.com/Deep3DVRSketch.

Character animation in real-world scenarios necessitates a variety of constraints such as trajectories key-frames interactions etc. Existing methodologies typically treat single or a finite set of these constraint(s) as separate control tasks. These methods are often specialized and the tasks they address are rarely extendable or customizable. We categorize these as solutions to the close-set motion control problem. In response to the complexity of practical motion control we propose and attempt to solve the open-set motion control problem. This problem is characterized by an open and fully customizable set of motion control tasks. To address this we introduce a new paradigm programmable motion generation. In this paradigm any given motion control task is broken down into a combination of atomic constraints. These constraints are then programmed into an error function that quantifies the degree to which a motion sequence adheres to them. We utilize a pre-trained motion generation model and optimize its latent code to minimize the error function of the generated motion. Consequently the generated motion not only inherits the prior of the generative model but also satisfies the requirements of the compounded constraints. Our experiments demonstrate that our approach can generate high-quality motions when addressing a wide range of unseen tasks. These tasks encompass motion control by motion dynamics geometric constraints physical laws interactions with scenes objects or the character's own body parts etc. All of these are achieved in a unified approach without the need for ad-hoc paired training data collection or specialized network designs. During the programming of novel tasks we observed the emergence of new skills beyond those of the prior model. With the assistance of large language models we also achieved automatic programming. We hope that this work will pave the way for the motion control of general AI agents.

Generating dances that are both lifelike and well-aligned with music continues to be a challenging task in the cross-modal domain. This paper introduces PopDanceSet the first dataset tailored to the preferences of young audiences enabling the generation of aesthetically oriented dances. And it surpasses the AIST++ dataset in music genre diversity and the intricacy and depth of dance movements. Moreover the proposed POPDG model within the iDDPM framework enhances dance diversity and through the Space Augmentation Algorithm strengthens spatial physical connections between human body joints ensuring that increased diversity does not compromise generation quality. A streamlined Alignment Module is also designed to improve the temporal alignment between dance and music. Extensive experiments show that POPDG achieves SOTA results on two datasets. Furthermore the paper also expands on current evaluation metrics. The dataset and code are available at https://github.com/Luke-Luo1/POPDG.

Modern video generation models like Sora have achieved remarkable success in producing high-quality videos. However a significant limitation is their inability to offer interactive control to users a feature that promises to open up unprecedented applications and creativity. In this work we introduce the first solution to equip diffusion-based video generation models with spatio-temporal control. We present Peekaboo a novel masked attention module which seamlessly integrates with current video generation models offering control without the need for additional training or inference overhead. To facilitate future research we also introduce a comprehensive benchmark for interactive video generation. This benchmark offers a standardized framework for the community to assess the efficacy of emerging interactive video generation models. Our extensive qualitative and quantitative assessments reveal that Peekaboo achieves up to a 3.8x improvement in mIoU over baseline models all while maintaining the same latency. Code and benchmark are available on the webpage.

Recent advancements in open-world 3D object generation have been remarkable with image-to-3D methods offering superior fine-grained control over their text-to-3D counterparts. However most existing models fall short in simultaneously providing rapid generation speeds and high fidelity to input images - two features essential for practical applications. In this paper we present One-2-3-45++ an innovative method that transforms a single image into a detailed 3D textured mesh in approximately one minute. Our approach aims to fully harness the extensive knowledge embedded in 2D diffusion models and priors from valuable yet limited 3D data. This is achieved by initially finetuning a 2D diffusion model for consistent multi-view image generation followed by elevating these images to 3D with the aid of multi-view-conditioned 3D native diffusion models. Extensive experimental evaluations demonstrate that our method can produce high-quality diverse 3D assets that closely mirror the original input image.

Scaling up model and data size has been quite successful for the evolution of LLMs. However the scaling law for the diffusion based text-to-image (T2I) models is not fully explored. It is also unclear how to efficiently scale the model for better performance at reduced cost. The different training settings and expensive training cost make a fair model comparison extremely difficult. In this work we empirically study the scaling properties of diffusion based T2I models by performing extensive and rigours ablations on scaling both denoising backbones and training set including training scaled UNet and Transformer variants ranging from 0.4B to 4B parameters on datasets upto 600M images. For model scaling we find the location and amount of cross attention distinguishes the performance of existing UNet designs. And increasing the transformer blocks is more parameter-efficient for improving text-image alignment than increasing channel numbers. We then identify an efficient UNet variant which is 45% smaller and 28% faster than SDXL's UNet. On the data scaling side we show the quality and diversity of the training set matters more than simply dataset size. Increasing caption density and diversity improves text-image alignment performance and the learning efficiency. Finally we provide scaling functions to predict the text-image alignment performance as functions of the scale of model size compute and dataset size.

Our paper aims to generate diverse and realistic animal motion sequences from textual descriptions without a large-scale animal text-motion dataset. While the task of text-driven human motion synthesis is already extensively studied and benchmarked it remains challenging to transfer this success to other skeleton structures with limited data. In this work we design a model architecture that imitates Generative Pretraining Transformer (GPT) utilizing prior knowledge learned from human data to the animal domain. We jointly train motion autoencoders for both animal and human motions and at the same time optimize through the similarity scores among human motion encoding animal motion encoding and text CLIP embedding. Presenting the first solution to this problem we are able to generate animal motions with high diversity and fidelity quantitatively and qualitatively outperforming the results of training human motion generation baselines on animal data. Additionally we introduce AnimalML3D the first text-animal motion dataset with 1240 animation sequences spanning 36 different animal identities. We hope this dataset would mediate the data scarcity problem in text-driven animal motion generation providing a new playground for the research community.

Dynamic Scene Graph Generation (DSGG) focuses on identifying visual relationships within the spatial-temporal domain of videos. Conventional approaches often employ multi-stage pipelines which typically consist of object detection temporal association and multi-relation classification. However these methods exhibit inherent limitations due to the separation of multiple stages and independent optimization of these sub-problems may yield sub-optimal solutions. To remedy these limitations we propose a one-stage end-to-end framework termed OED which streamlines the DSGG pipeline. This framework reformulates the task as a set prediction problem and leverages pair-wise features to represent each subject-object pair within the scene graph. Moreover another challenge of DSGG is capturing temporal dependencies we introduce a Progressively Refined Module (PRM) for aggregating temporal context without the constraints of additional trackers or handcrafted trajectories enabling end-to-end optimization of the network. Extensive experiments conducted on the Action Genome benchmark demonstrate the effectiveness of our design. The code and models are available at https://github.com/guanw-pku/OED.

Data-Free Knowledge Distillation (DFKD) has made significant recent strides by transferring knowledge from a teacher neural network to a student neural network without accessing the original data. Nonetheless existing approaches encounter a significant challenge when attempting to generate samples from random noise inputs which inherently lack meaningful information. Consequently these models struggle to effectively map this noise to the ground-truth sample distribution resulting in prolonging training times and low-quality outputs. In this paper we propose a novel Noisy Layer Generation method (NAYER) which relocates the random source from the input to a noisy layer and utilizes the meaningful constant label-text embedding (LTE) as the input. LTE is generated by using the language model once and then it is stored in memory for all subsequent training processes. The significance of LTE lies in its ability to contain substantial meaningful inter-class information enabling the generation of high-quality samples with only a few training steps. Simultaneously the noisy layer plays a key role in addressing the issue of diversity in sample generation by preventing the model from overemphasizing the constrained label information. By reinitializing the noisy layer in each iteration we aim to facilitate the generation of diverse samples while still retaining the method's efficiency thanks to the ease of learning provided by LTE. Experiments carried out on multiple datasets demonstrate that our NAYER not only outperforms the state-of-the-art methods but also achieves speeds 5 to 15 times faster than previous approaches. The code is available at https://github.com/tmtuan1307/nayer.

Despite significant advancements in text-to-motion synthesis generating language-guided human motion within 3D environments poses substantial challenges. These challenges stem primarily from (i) the absence of powerful generative models capable of jointly modeling natural language 3D scenes and human motion and (ii) the generative models' intensive data requirements contrasted with the scarcity of comprehensive high-quality language-scene-motion datasets. To tackle these issues we introduce a novel two-stage framework that employs scene affordance as an intermediate representation effectively linking 3D scene grounding and conditional motion generation. Our framework comprises an Affordance Diffusion Model (ADM) for predicting explicit affordance map and an Affordance-to-Motion Diffusion Model (AMDM) for generating plausible human motions. By leveraging scene affordance maps our method overcomes the difficulty in generating human motion under multimodal condition signals especially when training with limited data lacking extensive language-scene-motion pairs. Our extensive experiments demonstrate that our approach consistently outperforms all baselines on established benchmarks including HumanML3D and HUMANISE. Additionally we validate our model's exceptional generalization capabilities on a specially curated evaluation set featuring previously unseen descriptions and scenes.

Humans possess the capability to comprehend diverse modalities and seamlessly transfer information between them. In this work we introduce ModaVerse a Multi-modal Large Language Model (MLLM) capable of comprehending and transforming content across various modalities including images videos and audio. Predominant MLLM frameworks have largely relied on aligning latent spaces of textual and non-textual features. This alignment process which synchronizes a language model trained on textual data with encoders and decoders trained on multi-modal data often necessitates extensive training of several projection layers in multiple stages. Inspired by LLM-as-agent methodologies we propose a novel Input/Output (I/O) alignment mechanism that operates directly at the level of natural language. It aligns the LLM's output with the input of generative models avoiding the complexities associated with latent feature alignments and simplifying the multiple training stages of existing MLLMs into a single efficient process. By conducting experiments on several benchmarks we demonstrate that our approach attains comparable performance with the state of the art while achieving considerable efficiencies in data usage.

We introduce MeshGPT a new approach for generating triangle meshes that reflects the compactness typical of artist-created meshes in contrast to dense triangle meshes extracted by iso-surfacing methods from neural fields. Inspired by recent advances in powerful large language models we adopt a sequence-based approach to autoregressively generate triangle meshes as sequences of triangles. We first learn a vocabulary of latent quantized embeddings using graph convolutions which inform these embeddings of the local mesh geometry and topology. These embeddings are sequenced and decoded into triangles by a decoder ensuring that they can effectively reconstruct the mesh. A transformer is then trained on this learned vocabulary to predict the index of the next embedding given previous embeddings. Once trained our model can be autoregressively sampled to generate new triangle meshes directly generating compact meshes with sharp edges more closely imitating the efficient triangulation patterns of human-crafted meshes. MeshGPT demonstrates a notable improvement over state of the art mesh generation methods with a 9% increase in shape coverage and a 30-point enhancement in FID scores across various categories.

Creating high-quality materials in computer graphics is a challenging and time-consuming task which requires great expertise. To simplify this process we introduce MatFuse a unified approach that harnesses the generative power of diffusion models for creation and editing of 3D materials. Our method integrates multiple sources of conditioning including color palettes sketches text and pictures enhancing creative possibilities and granting fine-grained control over material synthesis. Additionally MatFuse enables map-level material editing capabilities through latent manipulation by means of a multi-encoder compression model which learns a disentangled latent representation for each map. We demonstrate the effectiveness of MatFuse under multiple conditioning settings and explore the potential of material editing. Finally we assess the quality of the generated materials both quantitatively in terms of CLIP-IQA and FID scores and qualitatively by conducting a user study. Source code for training MatFuse and supplemental materials are publicly available at https://gvecchio.com/matfuse.

Recent advancements in single image driven 3D content generation have been propelled by leveraging prior knowledge from pretrained 2D diffusion models. However the 3D content generated by existing methods often exhibits distorted outline shapes and inadequate details. To solve this problem we propose a novel framework called Mask-enhanced Progressive Outline-to-Detail optimization (aka. MPOD123) which consists of two stages. Specifically in the first stage MPOD123 utilizes the pretrained view-conditioned diffusion model to guide the outline shape optimization of the 3D content. Given certain viewpoint we estimate outline shape priors in the form of 2D mask from the 3D content by leveraging opacity calculation. In the second stage MPOD123 incorporates Detail Appearance Inpainting (DAI) to guide the refinement on local geometry and texture with the shape priors. The essence of DAI lies in the Mask Rectified Cross-Attention (MRCA) which can be conveniently plugged in the stable diffusion model. The MRCA module utilizes the mask to rectify the attention map from each cross-attention layer. Accompanied with this new module DAI is capable of guiding the detail refinement of the 3D content while better preserves the outline shape. To assess the applicability in practical scenarios we contribute a new dataset modeled on real-world e-commerce environments. Extensive quantitative and qualitative experiments on this dataset and open benchmarks demonstrate the effectiveness of MPOD123 over the state-of-the-arts.

We present a Multi-Instance Generation (MIG) task simultaneously generating multiple instances with diverse controls in one image. Given a set of predefined coordinates and their corresponding descriptions the task is to ensure that generated instances are accurately at the designated locations and that all instances' attributes adhere to their corresponding description. This broadens the scope of current research on Single-instance generation elevating it to a more versatile and practical dimension. Inspired by the idea of divide and conquer we introduce an innovative approach named Multi-Instance Generation Controller (MIGC) to address the challenges of the MIG task. Initially we break down the MIG task into several subtasks each involving the shading of a single instance. To ensure precise shading for each instance we introduce an instance enhancement attention mechanism. Lastly we aggregate all the shaded instances to provide the necessary information for accurately generating multiple instances in stable diffusion (SD). To evaluate how well generation models perform on the MIG task we provide a COCO-MIG benchmark along with an evaluation pipeline. Extensive experiments were conducted on the proposed COCO-MIG benchmark as well as on various commonly used benchmarks. The evaluation results illustrate the exceptional control capabilities of our model in terms of quantity position attribute and interaction. Code and demos will be released at https://migcproject.github.io/.

We introduce Multi-view Ancestral Sampling (MAS) a method for 3D motion generation using 2D diffusion models that were trained on motions obtained from in-the-wild videos. As such MAS opens opportunities to exciting and diverse fields of motion previously under-explored as 3D data is scarce and hard to collect. MAS works by simultaneously denoising multiple 2D motion sequences representing different views of the same 3D motion. It ensures consistency across all views at each diffusion step by combining the individual generations into a unified 3D sequence and projecting it back to the original views. We demonstrate MAS on 2D pose data acquired from videos depicting professional basketball maneuvers rhythmic gymnastic performances featuring a ball apparatus and horse races. In each of these domains 3D motion capture is arduous and yet MAS generates diverse and realistic 3D sequences. Unlike the Score Distillation approach which optimizes each sample by repeatedly applying small fixes our method uses a sampling process that was constructed for the diffusion framework. As we demonstrate MAS avoids common issues such as out-of-domain sampling and mode-collapse. https://guytevet.github.io/mas-page/

We propose Lodge a network capable of generating extremely long dance sequences conditioned on given music. We design Lodge as a two-stage coarse to fine diffusion architecture and propose the characteristic dance primitives that possess significant expressiveness as intermediate representations between two diffusion models. The first stage is global diffusion which focuses on comprehending the coarse-level music-dance correlation and production characteristic dance primitives. In contrast the second-stage is the local diffusion which parallelly generates detailed motion sequences under the guidance of the dance primitives and choreographic rules. In addition we propose a Foot Refine Block to optimize the contact between the feet and the ground enhancing the physical realism of the motion. Code available at https://li-ronghui.github.io/lodge

Scene Graph Generation (SGG) aims to identify entities and predict the relationship triplets <subject predicate object> in visual scenes. Given the prevalence of large visual variations of subject-object pairs even in the same predicate it can be quite challenging to model and refine predicate representations directly across such pairs which is however a common strategy adopted by most existing SGG methods. We observe that visual variations within the identical triplet are relatively small and certain relation cues are shared in the same type of triplet which can potentially facilitate the relation learning in SGG. Moreover for the long-tail problem widely studied in SGG task it is also crucial to deal with the limited types and quantity of triplets in tail predicates. Accordingly in this paper we propose a Dual-granularity Relation Modeling (DRM) network to leverage fine-grained triplet cues besides the coarse-grained predicate ones. DRM utilizes contexts and semantics of predicate and triplet with Dual-granularity Constraints generating compact and balanced representations from two perspectives to facilitate relation recognition. Furthermore a Dual-granularity Knowledge Transfer (DKT) strategy is introduced to transfer variation from head predicates/triplets to tail ones aiming to enrich the pattern diversity of tail classes to alleviate the long-tail problem. Extensive experiments demonstrate the effectiveness of our method which establishes new state-of-the-art performance on Visual Genome Open Image and GQA datasets. Our code is available at https://github.com/jkli1998/DRM.

Chain-of-Thought (CoT) guides large language models (LLMs) to reason step-by-step and can motivate their logical reasoning ability. While effective for logical tasks CoT is not conducive to creative problem-solving which often requires out-of-box thoughts and is crucial for innovation advancements. In this paper we explore the Leap-of-Thought (LoT) abilities within LLMs -- a non-sequential creative paradigm involving strong associations and knowledge leaps. To this end we study LLMs on the popular Oogiri game which needs participants to have good creativity and strong associative thinking for responding unexpectedly and humorously to the given image text or both and thus is suitable for LoT study. Then to investigate LLMs' LoT ability in the Oogiri game we first build a multimodal and multilingual Oogiri-GO dataset which contains over 130000 samples from the Oogiri game and observe the insufficient LoT ability or failures of most existing LLMs on the Oogiri game. Accordingly we introduce a creative Leap-of-Thought (CLoT) paradigm to improve LLM's LoT ability. CLoT first formulates the Oogiri-GO dataset into LoT-oriented instruction tuning data to train pretrained LLM for achieving certain LoT humor generation and discrimination abilities. Then CLoT designs an explorative self-refinement that encourages the LLM to generate more creative LoT data via exploring parallels between seemingly unrelated concepts and selects high-quality data to train itself for self-refinement. CLoT not only excels in humor generation in the Oogiri game as shown in Fig. 1 but also boosts creative abilities in various tasks like "cloud guessing game" and "divergent association task". These findings advance our understanding and offer a pathway to improve LLMs' creative capacities for innovative applications across domains. The dataset code and models have been released online: https://zhongshsh.github.io/CLoT.