CVPR 2024 Papers

Neural fields (NeFs) have recently emerged as a versatile method for modeling signals of various modalities including images shapes and scenes. Subsequently a number of works have explored the use of NeFs as representations for downstream tasks e.g. classifying an image based on the parameters of a NeF that has been fit to it. However the impact of the NeF hyperparameters on their quality as downstream representation is scarcely understood and remains largely unexplored. This is in part caused by the large amount of time required to fit datasets of neural fields.In this work we propose a JAX-based library that leverages parallelization to enable fast optimization of large-scale NeF datasets resulting in a significant speed-up. With this library we perform a comprehensive study that investigates the effects of different hyperparameters on fitting NeFs for downstream tasks. In particular we explore the use of a shared initialization the effects of overtraining and the expressiveness of the network architectures used. Our study provides valuable insights on how to train NeFs and offers guidance for optimizing their effectiveness in downstream applications. Finally based on the proposed library and our analysis we propose Neural Field Arena a benchmark consisting of neural field variants of popular vision datasets including MNIST CIFAR variants of ImageNet and ShapeNetv2. Our library and the Neural Field Arena will be open-sourced to introduce standardized benchmarking and promote further research on neural fields.

Recent years have seen immense progress in 3D computer vision and computer graphics with emerging tools that can virtualize real-world 3D environments for numerous Mixed Reality (XR) applications. However alongside immersive visual experiences immersive auditory experiences are equally vital to our holistic perception of an environment. In this paper we aim to reconstruct the spatial acoustic characteristics of an arbitrary environment given only a sparse set of (roughly 12) room impulse response (RIR) recordings and a planar reconstruction of the scene a setup that is easily achievable by ordinary users. To this end we introduce DiffRIR a differentiable RIR rendering framework with interpretable parametric models of salient acoustic features of the scene including sound source directivity and surface reflectivity. This allows us to synthesize novel auditory experiences through the space with any source audio. To evaluate our method we collect a dataset of RIR recordings and music in four diverse real environments. We show that our model outperforms state-of-the-art baselines on rendering monaural and binaural RIRs and music at unseen locations and learns physically interpretable parameters characterizing acoustic properties of the sound source and surfaces in the scene.

While modeling people wearing tight-fitting clothing has made great strides in recent years loose-fitting clothing remains a challenge. We propose a method that delivers realistic garment models from real-world images regardless of garment shape or deformation. To this end we introduce a fitting approach that utilizes shape and deformation priors learned from synthetic data to accurately capture garment shapes and deformations including large ones. Not only does our approach recover the garment geometry accurately it also yields models that can be directly used by downstream applications such as animation and simulation.

We propose functional diffusion a generative diffusion model focused on infinite-dimensional function data samples. In contrast to previous work functional diffusion works on samples that are represented by functions with a continuous domain. Functional diffusion can be seen as an extension of classical diffusion models to an infinite-dimensional domain. Functional diffusion is very versatile as images videos audio 3D shapes deformations etc. can be handled by the same framework with minimal changes. In addition functional diffusion is especially suited for irregular data or data defined in non-standard domains. In our work we derive the necessary foundations for functional diffusion and propose a first implementation based on the transformer architecture. We show generative results on complicated signed distance functions and deformation functions defined on 3D surfaces.

We present a framework for generating full-bodied photorealistic avatars that gesture according to the conversational dynamics of a dyadic interaction. Given speech audio we output multiple possibilities of gestural motion for an individual including face body and hands. The key behind our method is in combining the benefits of sample diversity from vector quantization with the high-frequency details obtained through diffusion to generate more dynamic expressive motion. We visualize the generated motion using highly photorealistic avatars that can express crucial nuances in gestures (e.g. sneers and smirks). To facilitate this line of research we introduce a first-of-its-kind multi-view conversational dataset that allows for photorealistic reconstruction. Experiments show our model generates appropriate and diverse gestures outperforming both diffusion- and VQ-only methods. Furthermore our perceptual evaluation highlights the importance of photorealism (vs. meshes) in accurately assessing subtle motion details in conversational gestures. Code and dataset available on project page.

The goal of this work is to simultaneously generate natural talking faces and speech outputs from text. We achieve this by integrating Talking Face Generation (TFG) and Text-to-Speech (TTS) systems into a unified framework. We address the main challenges of each task: (1) generating a range of head poses representative of real-world scenarios and (2) ensuring voice consistency despite variations in facial motion for the same identity. To tackle these issues we introduce a motion sampler based on conditional flow matching which is capable of high-quality motion code generation in an efficient way. Moreover we introduce a novel conditioning method for the TTS system which utilises motion-removed features from the TFG model to yield uniform speech outputs. Our extensive experiments demonstrate that our method effectively creates natural-looking talking faces and speech that accurately match the input text. To our knowledge this is the first effort to build a multimodal synthesis system that can generalise to unseen identities.

We introduce FaceTalk a novel generative approach designed for synthesizing high-fidelity 3D motion sequences of talking human heads from input audio signal. To capture the expressive detailed nature of human heads including hair ears and finer-scale eye movements we propose to couple speech signal with the latent space of neural parametric head models to create high-fidelity temporally coherent motion sequences. We propose a new latent diffusion model for this task operating in the expression space of neural parametric head models to synthesize audio-driven realistic head sequences. In the absence of a dataset with corresponding NPHM expressions to audio we optimize for these correspondences to produce a dataset of temporally-optimized NPHM expressions fit to audio-video recordings of people talking. To the best of our knowledge this is the first work to propose a generative approach for realistic and high-quality motion synthesis of volumetric human heads representing a significant advancement in the field of audio-driven 3D animation. Notably our approach stands out in its ability to generate plausible motion sequences that can produce high-fidelity head animation coupled with the NPHM shape space. Our experimental results substantiate the effectiveness of FaceTalk consistently achieving superior and visually natural motion encompassing diverse facial expressions and styles outperforming existing methods by 75% in perceptual user study evaluation

In this paper we abstract the process of people hearing speech extracting meaningful cues and creating various dynamically audio-consistent talking faces termed Listening and Imagining into the task of high-fidelity diverse talking faces generation from a single audio. Specifically it involves two critical challenges: one is to effectively decouple identity content and emotion from entangled audio and the other is to maintain intra-video diversity and inter-video consistency. To tackle the issues we first dig out the intricate relationships among facial factors and simplify the decoupling process tailoring a Progressive Audio Disentanglement for accurate facial geometry and semantics learning where each stage incorporates a customized training module responsible for a specific factor. Secondly to achieve visually diverse and audio-synchronized animation solely from input audio within a single model we introduce the Controllable Coherent Frame generation which involves the flexible integration of three trainable adapters with frozen Latent Diffusion Models (LDMs) to focus on maintaining facial geometry and semantics as well as texture and temporal coherence between frames. In this way we inherit high-quality diverse generation from LDMs while significantly improving their controllability at a low training cost. Extensive experiments demonstrate the flexibility and effectiveness of our method in handling this paradigm. The codes will be released at https://github.com/modelscope/facechain.

Existing methods for synthesizing 3D human gestures from speech have shown promising results but they do not explicitly model the impact of emotions on the generated gestures. Instead these methods directly output animations from speech without control over the expressed emotion. To address this limitation we present AMUSE an emotional speech-driven body animation model based on latent diffusion. Our observation is that content (i.e. gestures related to speech rhythm and word utterances) emotion and personal style are separable. To account for this AMUSE maps the driving audio to three disentangled latent vectors: one for content one for emotion and one for personal style. A latent diffusion model trained to generate gesture motion sequences is then conditioned on these latent vectors. Once trained AMUSE synthesizes 3D human gestures directly from speech with control over the expressed emotions and style by combining the content from the driving speech with the emotion and style of another speech sequence. Randomly sampling the noise of the diffusion model further generates variations of the gesture with the same emotional expressivity. Qualitative quantitative and perceptual evaluations demonstrate that AMUSE outputs realistic gesture sequences. Compared to the state of the art the generated gestures are better synchronized with the speech content and better represent the emotion expressed by the input speech. Our code is available at amuse.is.tue.mpg.de.

We present Ego-Exo4D a diverse large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric video of skilled human activities (e.g. sports music dance bike repair). 740 participants from 13 cities worldwide performed these activities in 123 different natural scene contexts yielding long-form captures from 1 to 42 minutes each and 1286 hours of video combined. The multimodal nature of the dataset is unprecedented: the video is accompanied by multichannel audio eye gaze 3D point clouds camera poses IMU and multiple paired language descriptions---including a novel "expert commentary" done by coaches and teachers and tailored to the skilled-activity domain. To push the frontier of first-person video understanding of skilled human activity we also present a suite of benchmark tasks and their annotations including fine-grained activity understanding proficiency estimation cross-view translation and 3D hand/body pose. All resources are open sourced to fuel new research in the community.

We propose a novel strategy ES3 for self-supervised learning of robust audio-visual speech representations from unlabeled talking face videos. While many recent approaches for this task primarily rely on guiding the learning process using the audio modality alone to capture information shared between audio and video we reframe the problem as the acquisition of shared unique (modality-specific) and synergistic speech information to address the inherent asymmetry between the modalities. Based on this formulation we propose a novel "evolving" strategy that progressively builds joint audio-visual speech representations that are strong for both uni-modal (audio & visual) and bi-modal (audio-visual) speech. First we leverage the more easily learnable audio modality to initialize audio and visual representations by capturing audio-unique and shared speech information. Next we incorporate video-unique speech information and bootstrap the audio-visual representations on top of the previously acquired shared knowledge. Finally we maximize the total audio-visual speech information including synergistic information to obtain robust and comprehensive representations. We implement ES3 as a simple Siamese framework and experiments on both English benchmarks and a newly contributed large-scale Mandarin dataset show its effectiveness. In particular on LRS2-BBC our smallest model is on par with SoTA models with only 1/2 parameters and 1/8 unlabeled data (223h).

We propose EMAGE a framework to generate full-body human gestures from audio and masked gestures encompassing facial local body hands and global movements. To achieve this we first introduce BEAT2 (BEAT-SMPLX-FLAME) a new mesh-level holistic co-speech dataset. BEAT2 combines a MoShed SMPL-X body with FLAME head parameters and further refines the modeling of head neck and finger movements offering a community-standardized high-quality 3D motion captured dataset. EMAGE leverages masked body gesture priors during training to boost inference performance. It involves a Masked Audio Gesture Transformer facilitating joint training on audio-to-gesture generation and masked gesture reconstruction to effectively encode audio and body gesture hints. Encoded body hints from masked gestures are then separately employed to generate facial and body movements. Moreover EMAGE adaptively merges speech features from the audio's rhythm and content and utilizes four compositional VQ-VAEs to enhance the results' fidelity and diversity. Experiments demonstrate that EMAGE generates holistic gestures with state-of-the-art performance and is flexible in accepting predefined spatial-temporal gesture inputs generating complete audio-synchronized results. Our code and dataset are available. https://pantomatrix.github.io/EMAGE/

Audio-visual saliency prediction can draw support from diverse modality complements but further performance enhancement is still challenged by customized architectures as well as task-specific loss functions. In recent studies denoising diffusion models have shown more promising in unifying task frameworks owing to their inherent ability of generalization. Following this motivation a novel Diffusion architecture for generalized audio-visual Saliency prediction (DiffSal) is proposed in this work which formulates the prediction problem as a conditional generative task of the saliency map by utilizing input audio and video as the conditions. Based on the spatio-temporal audio-visual features an extra network Saliency-UNet is designed to perform multi-modal attention modulation for progressive refinement of the ground-truth saliency map from the noisy map. Extensive experiments demonstrate that the proposed DiffSal can achieve excellent performance across six challenging audio-visual benchmarks with an average relative improvement of 6.3% over the previous state-of-the-art results by six metrics.

We propose DiffSHEG a Diffusion-based approach for Speech-driven Holistic 3D Expression and Gesture generation. While previous works focused on co-speech gesture or expression generation individually the joint generation of synchronized expressions and gestures remains barely explored. To address this our diffusion-based co-speech motion generation Transformer enables uni-directional information flow from expression to gesture facilitating improved matching of joint expression-gesture distributions. Furthermore we introduce an outpainting-based sampling strategy for arbitrary long sequence generation in diffusion models offering flexibility and computational efficiency. Our method provides a practical solution that produces high-quality synchronized expression and gesture generation driven by speech. Evaluated on two public datasets our approach achieves state-of-the-art performance both quantitatively and qualitatively. Additionally a user study confirms the superiority of our method over prior approaches. By enabling the real-time generation of expressive and synchronized motions our method showcases its potential for various applications in the development of digital humans and embodied agents.

Synchronization issues between audio and video are one of the most disturbing quality defects in film production and live broadcasting. Even a discrepancy as short as 45 millisecond can degrade the viewer's experience enough to warrant manual quality checks over entire movies. In this paper we study the automatic discovery of such issues. Specifically we focus on the alignment of lip movements with spoken words targeting realistic production scenarios which can include background noise and music intricate head poses excessive makeup or scenes with multiple individuals where the speaker is unknown. Our model's robustness also extends to various media specifications including different video frame rates and audio sample rates. To address these challenges we present a model fully based on transformers that encodes face crops or full video frames and raw audio using timestamp information identifies the speaker and provides highly accurate synchronization predictions much faster than previous methods.

Binaural audio is obtained by simulating the biological structure of human ears which plays an important role in artificial immersive spaces. A promising approach is to utilize mono audio and corresponding vision to synthesize binaural audio thereby avoiding expensive binaural audio recording. However most existing methods directly use the entire scene as a guide ignoring the correspondence between sounds and sounding objects. In this paper we advocate generating binaural audio using fine-grained raw waveform and object-level visual information as guidance. Specifically we propose a Cyclic Locating-and-UPmixing (CLUP) framework that jointly learns visual sounding object localization and binaural audio generation. Visual sounding object localization establishes the correspondence between specific visual objects and sound modalities which provides object-aware guidance to improve binaural generation performance. Meanwhile the spatial information contained in the generated binaural audio can further improve the performance of sounding object localization. In this case visual sounding object localization and binaural audio generation can achieve cyclic learning and benefit from each other. Experimental results demonstrate that on the FAIR-Play benchmark dataset our method is significantly ahead of the existing baselines in multiple evaluation metrics (STFT\downarrow: 0.787 vs. 0.851 ENV\downarrow: 0.128 vs. 0.134 WAV\downarrow: 5.244 vs. 5.684 SNR\uparrow: 7.546 vs. 7.044).

Gestures play a key role in human communication. Recent methods for co-speech gesture generation while managing to generate beat-aligned motions struggle generating gestures that are semantically aligned with the utterance. Compared to beat gestures that align naturally to the audio signal semantically coherent gestures require modeling the complex interactions between the language and human motion and can be controlled by focusing on certain words. Therefore we present ConvoFusion a diffusion-based approach for multi-modal gesture synthesis which can not only generate gestures based on multi-modal speech inputs but can also facilitate controllability in gesture synthesis. Our method proposes two guidance objectives that allow the users to modulate the impact of different conditioning modalities (e.g. audio vs text) as well as to choose certain words to be emphasized during gesturing. Our method is versatile in that it can be trained either for generating monologue gestures or even the conversational gestures. To further advance the research on multi-party interactive gestures the DnD Group Gesture dataset is released which contains 6 hours of gesture data showing 5 people interacting with one another. We compare our method with several recent works and demonstrate effectiveness of our method on a variety of tasks. We urge the reader to watch our supplementary video at https://vcai.mpi-inf.mpg.de/projects/ConvoFusion/.

Co-speech gestures if presented in the lively form of videos can achieve superior visual effects in human-machine interaction. While previous works mostly generate structural human skeletons resulting in the omission of appearance information we focus on the direct generation of audio-driven co-speech gesture videos in this work. There are two main challenges: 1) A suitable motion feature is needed to describe complex human movements with crucial appearance information. 2) Gestures and speech exhibit inherent dependencies and should be temporally aligned even of arbitrary length. To solve these problems we present a novel motion-decoupled framework to generate co-speech gesture videos. Specifically we first introduce a well-designed nonlinear TPS transformation to obtain latent motion features preserving essential appearance information. Then a transformer-based diffusion model is proposed to learn the temporal correlation between gestures and speech and performs generation in the latent motion space followed by an optimal motion selection module to produce long-term coherent and consistent gesture videos. For better visual perception we further design a refinement network focusing on missing details of certain areas. Extensive experimental results show that our proposed framework significantly outperforms existing approaches in both motion and video-related evaluations. Our code demos and more resources are available at https://github.com/thuhcsi/S2G-MDDiffusion.

Existing audio-visual segmentation datasets typically focus on short-trimmed videos with only one pixel-map annotation for a per-second video clip. In contrast for untrimmed videos the sound duration start- and end-sounding time positions and visual deformation of audible objects vary significantly. Therefore we observed that current AVS models trained on trimmed videos might struggle to segment sounding objects in long videos. To investigate the feasibility of grounding audible objects in videos along both temporal and spatial dimensions we introduce the Long-Untrimmed Audio-Visual Segmentation dataset (LU-AVS) which includes precise frame-level annotations of sounding emission times and provides exhaustive mask annotations for all frames. Considering that pixel-level annotations are difficult to achieve in some complex scenes we also provide the bounding boxes to indicate the sounding regions. Specifically LU-AVS contains 10M mask annotations across 6.6K videos and 11M bounding box annotations across 7K videos. Compared with the existing datasets LU-AVS videos are on average 4 8 times longer with the silent duration being 3 15 times greater. Furthermore we try our best to adapt some baseline models that were originally designed for audio-visual-relevant tasks to examine the challenges of our newly curated LU-AVS. Through comprehensive evaluation we demonstrate the challenges of LU-AVS compared to the ones containing trimmed videos. Therefore LU-AVS provides an ideal yet challenging platform for evaluating audio-visual segmentation and localization on untrimmed long videos. The dataset is publicly available at: https://yenanliu.github.io/LU-AVS/.