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Found 26 papers, 12 papers with code
XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques
To address this, we introduce XRL-Bench, a unified standardized benchmark tailored for the evaluation and comparison of XRL methods, encompassing three main modules: standard RL environments, explainers based on state importance, and standard evaluators.
Towards Efficient Diffusion-Based Image Editing with Instant Attention Masks
In particular, InstDiffEdit aims to employ the cross-modal attention ability of existing diffusion models to achieve instant mask guidance during the diffusion steps.
Towards a Simultaneous and Granular Identity-Expression Control in Personalized Face Generation
We devise a novel diffusion model that can undertake the task of simultaneously face swapping and reenactment.
Text-Guided 3D Face Synthesis -- From Generation to Editing
In the editing stage, we first employ a pre-trained diffusion model to update facial geometry or texture based on the texts.
AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model
Aligning agent behaviors with diverse human preferences remains a challenging problem in reinforcement learning (RL), owing to the inherent abstractness and mutability of human preferences.
EfficientDreamer: High-Fidelity and Robust 3D Creation via Orthogonal-view Diffusion Prior
This limitation leads to the Janus problem, where multi-faced 3D models are generated under the guidance of such diffusion models.
Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations
In this paper, we present an end-to-end framework Structure-CLIP, which integrates Scene Graph Knowledge (SGK) to enhance multi-modal structured representations.
TalkCLIP: Talking Head Generation with Text-Guided Expressive Speaking Styles
In this work, we propose an expression-controllable one-shot talking head method, dubbed TalkCLIP, where the expression in a speech is specified by the natural language.
2D Semantic Segmentation task 3 (25 classes)
Talking Head Generation
NeFII: Inverse Rendering for Reflectance Decomposition with Near-Field Indirect Illumination
Inverse rendering methods aim to estimate geometry, materials and illumination from multi-view RGB images.
Ranked #2 on
Surface Normals Estimation
on Stanford-ORB
DINet: Deformation Inpainting Network for Realistic Face Visually Dubbing on High Resolution Video
Different from previous works relying on multiple up-sample layers to directly generate pixels from latent embeddings, DINet performs spatial deformation on feature maps of reference images to better preserve high-frequency textural details.
Zero-Shot Text-to-Parameter Translation for Game Character Auto-Creation
In our method, taking the power of large-scale pre-trained multi-modal CLIP and neural rendering, T2P searches both continuous facial parameters and discrete facial parameters in a unified framework.
Tailoring Language Generation Models under Total Variation Distance
The standard paradigm of neural language generation adopts maximum likelihood estimation (MLE) as the optimizing method.
StyleTalk: One-shot Talking Head Generation with Controllable Speaking Styles
In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio.
Towards Unbiased Volume Rendering of Neural Implicit Surfaces With Geometry Priors
In this paper, we argue that this limited accuracy is due to the bias of their volume rendering strategies, especially when the viewing direction is close to be tangent to the surface.
TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective
TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions which further improves estimation accuracy.
Facial Action Unit Detection and Intensity Estimation from Self-supervised Representation
As a fine-grained and local expression behavior measurement, facial action unit (FAU) analysis (e. g., detection and intensity estimation) has been documented for its time-consuming, labor-intensive, and error-prone annotation.
Facial Action Units Detection Aided by Global-Local Expression Embedding
We find that AUs and facial expressions are highly associated, and existing facial expression datasets often contain a large number of identities.
Generating Coherent Narratives by Learning Dynamic and Discrete Entity States with a Contrastive Framework
Despite advances in generating fluent texts, existing pretraining models tend to attach incoherent event sequences to involved entities when generating narratives such as stories and news.
LaMemo: Language Modeling with Look-Ahead Memory
Although Transformers with fully connected self-attentions are powerful to model long-term dependencies, they are struggling to scale to long texts with thousands of words in language modeling.
I-Tuning: Tuning Frozen Language Models with Image for Lightweight Image Captioning
Different to these heavy-cost models, we introduce a lightweight image captioning framework (I-Tuning), which contains a small number of trainable parameters.
Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games
With this unified diversity measure, we design the corresponding diversity-promoting objective and population effectivity when seeking the best responses in open-ended learning.
Neural-to-Tree Policy Distillation with Policy Improvement Criterion
The empirical results show that the proposed method can preserve a higher cumulative reward than behavior cloning and learn a more consistent policy to the original one.
KuiLeiXi: a Chinese Open-Ended Text Adventure Game
Equipped with GPT-2 and the latest GPT-3, AI Dungeon has been seen as a famous example of the powerful text generation capabilities of large-scale pre-trained language models, and a possibility for future games.
GLIB: Towards Automated Test Oracle for Graphically-Rich Applications
We perform an evaluation of \texttt{GLIB} on 20 real-world game apps (with bug reports available) and the result shows that \texttt{GLIB} can achieve 100\% precision and 99. 5\% recall in detecting non-crashing bugs such as game GUI glitches.
Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games
With this unified diversity measure, we design the corresponding diversity-promoting objective and population effectivity when seeking the best responses in open-ended learning.
Learning Action-Transferable Policy with Action Embedding
Transfer learning (TL) is a promising way to improve the sample efficiency of reinforcement learning.
