Talking head synthesis with arbitrary speech audio is a crucial challenge in the field of digital humans. Recently, methods based on radiance fields have received increasing attention due to their ability to synthesize high-fidelity and identity-consistent talking heads from just a few minutes of training video. However, due to the limited scale of the training data, these methods often exhibit poor performance in audio-lip synchronization and visual quality. In this paper, we propose a novel 3D Gaussian-based method called PointTalk, which constructs a static 3D Gaussian field of the head and deforms it in sync with the audio. It also incorporates an audio-driven dynamic lip point cloud as a critical component of the conditional information, thereby facilitating the effective synthesis of talking heads. Specifically, the initial step involves generating the corresponding lip point cloud from the audio signal and capturing its topological structure. The design of the dynamic difference encoder aims to capture the subtle nuances inherent in dynamic lip movements more effectively. Furthermore, we integrate the audio-point enhancement module, which not only ensures the synchronization of the audio signal with the corresponding lip point cloud within the feature space, but also facilitates a deeper understanding of the interrelations among cross-modal conditional features. Extensive experiments demonstrate that our method achieves superior high-fidelity and audio-lip synchronization in talking head synthesis compared to previous methods.

PointTalk: Audio-Driven Dynamic Lip Point Cloud for 3D Gaussian-based Talking Head Synthesis

Pose-controlled human video generation is of significant interest and finds extensive applications in areas such as automated advertising and content creation on social media platforms. While existing methods employing pose sequences and reference images for human image animation have exhibited notable performance, they tend to encounter issues such as specific region blurring, background sharpening, and decreased identity consistency. In this paper, we introduce ReMask-Animate, which utilizes masks as additional priors to guide the model's local visual attention to specific areas, thereby alleviating feature confusion between different regions of the image. Three distinct mask-guided adapters are designed for cross-condition regional fusion of hand and face pose features, mitigating feature confusion between the foreground and background, and enhancing the visual consistency of character identity. Moreover, these lightweight adapters introduce minimal computational overhead and can be seamlessly integrated into specific layers of the backbone architecture. Extensive experiments show that our method outperforms state-of-the-art methods on five metrics in public datasets. Additionally, qualitative evaluations highlight a significant improvement in the quality of generated videos, demonstrating our approach's superiority.

ReMask-Animate: Refined Character Image Animation Using Mask-Guided Adapters

Reasoning, a fundamental cognitive process integral to human intelligence, has garnered substantial interest within artificial intelligence.
Notably, recent studies have revealed that chain-of-thought prompting significantly enhances LLM's reasoning capabilities, which attracts widespread attention from both academics and industry.
In this paper, we systematically investigate relevant research, summarizing advanced methods through a meticulous taxonomy that offers novel perspectives.
Moreover, we delve into the current frontiers and delineate the challenges and future directions, thereby shedding light on future research.
Furthermore, we engage in a discussion about open questions.
We hope this paper serves as an introduction for beginners and fosters future research.
Resources have been made publicly available at https://github.com/zchuz/CoT-Reasoning-Survey

Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future

Grasping the concept of time is a fundamental facet of human cognition, indispensable for truly comprehending the intricacies of the world.
Previous studies typically focus on specific aspects of time, lacking a comprehensive temporal reasoning benchmark.
To address this, we propose TimeBench, a comprehensive hierarchical temporal reasoning benchmark that covers a broad spectrum of temporal reasoning phenomena.
TimeBench provides a thorough evaluation for investigating the temporal reasoning capabilities of large language models.
We conduct extensive experiments on GPT-4, LLaMA2, and other popular LLMs under various settings.
Our experimental results indicate a significant performance gap between the state-of-the-art LLMs and humans, highlighting that there is still a considerable distance to cover in temporal reasoning.
Besides, LLMs exhibit capability discrepancies across different reasoning categories.
Furthermore, we thoroughly analyze the impact of multiple aspects on temporal reasoning and emphasize the associated challenges.
We aspire for TimeBench to serve as a comprehensive benchmark, fostering research in temporal reasoning.
Code and data are available at https://github.com/zchuz/TimeBench.

TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models

The recent algorithms for math word problems (MWP) neglect to use outside knowledge not present in the problems. Most of them only capture the word-level relationship and ignore building hierarchical reasoning like the human being for mining the contextual structure between words and sentences. In this paper, we propose a Reasoning with Pre-trained Knowledge and Hierarchical Structure (RPKHS) network, which contains a pre-trained knowledge encoder and a hierarchical reasoning encoder. Firstly, our pre-trained knowledge encoder aims at reasoning the MWP by using outside knowledge from the pre-trained transformer-based models. Secondly, the hierarchical reasoning encoder is presented for seamlessly integrating the wordlevel and sentence-level reasoning to bridge the entity and context domain on MWP. Extensive experiments show that our RPKHS significantly outperforms state-of-the-art approaches on two large-scale commonly-used datasets, and boosts performance from 77.4% to 83.9% on Math23K, from 75.5 to 82.2% on Math23K with 5-fold cross-validation and from 83.7% to 89.8% on MAWPS. More extensive ablations are shown to demonstrate the effectiveness and interpretability of our proposed method.

Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning

Papers available for asynchronous viewing.

Computer Vision (CV)

poster

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

AAAI 2025

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



This poster session includes Main Conference posters, TACL and Demos.

In-Person Poster Session 5

### Welcome!
The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. Our Virtual Poster Sessions will take place online Thursday, August 22, 2024.

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Please register!

ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

Virtual Poster Session I: NLP Applications

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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Access is given only to EMNLP upon registration, if you have not registered please do so [here](https://2021.emnlp.org/registration).

Registered attendees will receive access the week of November 1st.

EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Weijiang Yu

5

2

SHORT BIO

Presentations

PointTalk: Audio-Driven Dynamic Lip Point Cloud for 3D Gaussian-based Talking Head Synthesis

ReMask-Animate: Refined Character Image Animation Using Mask-Guided Adapters

Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future

TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models

Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning

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