4,430 research outputs found
PMMTalk: Speech-Driven 3D Facial Animation from Complementary Pseudo Multi-modal Features
Speech-driven 3D facial animation has improved a lot recently while most
related works only utilize acoustic modality and neglect the influence of
visual and textual cues, leading to unsatisfactory results in terms of
precision and coherence. We argue that visual and textual cues are not trivial
information. Therefore, we present a novel framework, namely PMMTalk, using
complementary Pseudo Multi-Modal features for improving the accuracy of facial
animation. The framework entails three modules: PMMTalk encoder, cross-modal
alignment module, and PMMTalk decoder. Specifically, the PMMTalk encoder
employs the off-the-shelf talking head generation architecture and speech
recognition technology to extract visual and textual information from speech,
respectively. Subsequently, the cross-modal alignment module aligns the
audio-image-text features at temporal and semantic levels. Then PMMTalk decoder
is employed to predict lip-syncing facial blendshape coefficients. Contrary to
prior methods, PMMTalk only requires an additional random reference face image
but yields more accurate results. Additionally, it is artist-friendly as it
seamlessly integrates into standard animation production workflows by
introducing facial blendshape coefficients. Finally, given the scarcity of 3D
talking face datasets, we introduce a large-scale 3D Chinese Audio-Visual
Facial Animation (3D-CAVFA) dataset. Extensive experiments and user studies
show that our approach outperforms the state of the art. We recommend watching
the supplementary video
Capture, Learning, and Synthesis of 3D Speaking Styles
Audio-driven 3D facial animation has been widely explored, but achieving
realistic, human-like performance is still unsolved. This is due to the lack of
available 3D datasets, models, and standard evaluation metrics. To address
this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans
captured at 60 fps and synchronized audio from 12 speakers. We then train a
neural network on our dataset that factors identity from facial motion. The
learned model, VOCA (Voice Operated Character Animation) takes any speech
signal as input - even speech in languages other than English - and
realistically animates a wide range of adult faces. Conditioning on subject
labels during training allows the model to learn a variety of realistic
speaking styles. VOCA also provides animator controls to alter speaking style,
identity-dependent facial shape, and pose (i.e. head, jaw, and eyeball
rotations) during animation. To our knowledge, VOCA is the only realistic 3D
facial animation model that is readily applicable to unseen subjects without
retargeting. This makes VOCA suitable for tasks like in-game video, virtual
reality avatars, or any scenario in which the speaker, speech, or language is
not known in advance. We make the dataset and model available for research
purposes at http://voca.is.tue.mpg.de.Comment: To appear in CVPR 201
Dynamic Facial Expression of Emotion Made Easy
Facial emotion expression for virtual characters is used in a wide variety of
areas. Often, the primary reason to use emotion expression is not to study
emotion expression generation per se, but to use emotion expression in an
application or research project. What is then needed is an easy to use and
flexible, but also validated mechanism to do so. In this report we present such
a mechanism. It enables developers to build virtual characters with dynamic
affective facial expressions. The mechanism is based on Facial Action Coding.
It is easy to implement, and code is available for download. To show the
validity of the expressions generated with the mechanism we tested the
recognition accuracy for 6 basic emotions (joy, anger, sadness, surprise,
disgust, fear) and 4 blend emotions (enthusiastic, furious, frustrated, and
evil). Additionally we investigated the effect of VC distance (z-coordinate),
the effect of the VC's face morphology (male vs. female), the effect of a
lateral versus a frontal presentation of the expression, and the effect of
intensity of the expression. Participants (n=19, Western and Asian subjects)
rated the intensity of each expression for each condition (within subject
setup) in a non forced choice manner. All of the basic emotions were uniquely
perceived as such. Further, the blends and confusion details of basic emotions
are compatible with findings in psychology
DiffusionTalker: Personalization and Acceleration for Speech-Driven 3D Face Diffuser
Speech-driven 3D facial animation has been an attractive task in both
academia and industry. Traditional methods mostly focus on learning a
deterministic mapping from speech to animation. Recent approaches start to
consider the non-deterministic fact of speech-driven 3D face animation and
employ the diffusion model for the task. However, personalizing facial
animation and accelerating animation generation are still two major limitations
of existing diffusion-based methods. To address the above limitations, we
propose DiffusionTalker, a diffusion-based method that utilizes contrastive
learning to personalize 3D facial animation and knowledge distillation to
accelerate 3D animation generation. Specifically, to enable personalization, we
introduce a learnable talking identity to aggregate knowledge in audio
sequences. The proposed identity embeddings extract customized facial cues
across different people in a contrastive learning manner. During inference,
users can obtain personalized facial animation based on input audio, reflecting
a specific talking style. With a trained diffusion model with hundreds of
steps, we distill it into a lightweight model with 8 steps for acceleration.
Extensive experiments are conducted to demonstrate that our method outperforms
state-of-the-art methods. The code will be released
RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars
Synthesizing high-fidelity head avatars is a central problem for computer
vision and graphics. While head avatar synthesis algorithms have advanced
rapidly, the best ones still face great obstacles in real-world scenarios. One
of the vital causes is inadequate datasets -- 1) current public datasets can
only support researchers to explore high-fidelity head avatars in one or two
task directions; 2) these datasets usually contain digital head assets with
limited data volume, and narrow distribution over different attributes. In this
paper, we present RenderMe-360, a comprehensive 4D human head dataset to drive
advance in head avatar research. It contains massive data assets, with 243+
million complete head frames, and over 800k video sequences from 500 different
identities captured by synchronized multi-view cameras at 30 FPS. It is a
large-scale digital library for head avatars with three key attributes: 1) High
Fidelity: all subjects are captured by 60 synchronized, high-resolution 2K
cameras in 360 degrees. 2) High Diversity: The collected subjects vary from
different ages, eras, ethnicities, and cultures, providing abundant materials
with distinctive styles in appearance and geometry. Moreover, each subject is
asked to perform various motions, such as expressions and head rotations, which
further extend the richness of assets. 3) Rich Annotations: we provide
annotations with different granularities: cameras' parameters, matting, scan,
2D/3D facial landmarks, FLAME fitting, and text description.
Based on the dataset, we build a comprehensive benchmark for head avatar
research, with 16 state-of-the-art methods performed on five main tasks: novel
view synthesis, novel expression synthesis, hair rendering, hair editing, and
talking head generation. Our experiments uncover the strengths and weaknesses
of current methods. RenderMe-360 opens the door for future exploration in head
avatars.Comment: Technical Report; Project Page: 36; Github Link:
https://github.com/RenderMe-360/RenderMe-36
A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation
Body language (BL) refers to the non-verbal communication expressed through
physical movements, gestures, facial expressions, and postures. It is a form of
communication that conveys information, emotions, attitudes, and intentions
without the use of spoken or written words. It plays a crucial role in
interpersonal interactions and can complement or even override verbal
communication. Deep multi-modal learning techniques have shown promise in
understanding and analyzing these diverse aspects of BL. The survey emphasizes
their applications to BL generation and recognition. Several common BLs are
considered i.e., Sign Language (SL), Cued Speech (CS), Co-speech (CoS), and
Talking Head (TH), and we have conducted an analysis and established the
connections among these four BL for the first time. Their generation and
recognition often involve multi-modal approaches. Benchmark datasets for BL
research are well collected and organized, along with the evaluation of SOTA
methods on these datasets. The survey highlights challenges such as limited
labeled data, multi-modal learning, and the need for domain adaptation to
generalize models to unseen speakers or languages. Future research directions
are presented, including exploring self-supervised learning techniques,
integrating contextual information from other modalities, and exploiting
large-scale pre-trained multi-modal models. In summary, this survey paper
provides a comprehensive understanding of deep multi-modal learning for various
BL generations and recognitions for the first time. By analyzing advancements,
challenges, and future directions, it serves as a valuable resource for
researchers and practitioners in advancing this field. n addition, we maintain
a continuously updated paper list for deep multi-modal learning for BL
recognition and generation: https://github.com/wentaoL86/awesome-body-language
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