New benchmarks and datasets advance AI image and video generation
ByPulseAugur Editorial·[37 sources]·
Researchers are developing new benchmarks and datasets to advance text-to-image and text-to-video generation models. One paper introduces GPIC, a massive, permissively licensed image corpus for visual generation, while another proposes LoCoT2V-Bench for evaluating long-form, complex text-to-video generation. Additionally, new methods are emerging for evaluating fairness and alignment in text-to-image models, and for improving the efficiency and quality of discrete text-to-image generation.
AI
IMPACT
New datasets and evaluation frameworks will accelerate research and development in multimodal AI generation, pushing the boundaries of image and video synthesis.
RANK_REASON
Multiple research papers introducing new datasets, benchmarks, and evaluation methodologies for AI generation tasks.
arXiv:2503.07265v4 Announce Type: replace-cross Abstract: Text-to-Image (T2I) models are capable of generating high-quality artistic creations and visual content. However, existing research and evaluation standards predominantly focus on image realism and shallow text-image align…
arXiv cs.LG
TIER_1English(EN)·Farbod Davoodi, Seyed Reza Tavakoli Shiyadeh, Pooria Safaei, Sana Harighi, Parsa Gholami, Amirali Amini, Kimia Vanaei, Emad Firoozi, Parham Abed Azad, Babak Khalaj, Siavash Ahmadi, Amir Hossein Payberah, Mohammad Hossein Rohban, Soheil Kolouri, Ali Diba·
arXiv:2606.01282v1 Announce Type: cross Abstract: Text-to-Image (TTI) systems are now everyday infrastructure for journalism, education, advertising, and public communication, and the demographic and cultural stereotypes they inherit from training data (rendering women, people of…
arXiv cs.AI
TIER_1English(EN)·Keshigeyan Chandrasegaran, Kyle Sargent, Suchir Agarwal, Michael Jang, Michael Poli, Juan Carlos Niebles, Justin Johnson, Jiajun Wu, Li Fei-Fei·
arXiv:2605.30341v1 Announce Type: cross Abstract: Studying scalable methods for visual generative modeling requires large, accessible, and stable datasets. We introduce GPIC, a Giant Permissive Image Corpus of approximately 28 trillion pixels. GPIC comprises diverse internet imag…
arXiv cs.AI
TIER_1English(EN)·Xiangqing Zheng, Chengyue Wu, Kehai Chen, Min Zhang·
arXiv:2510.26412v3 Announce Type: replace-cross Abstract: Recent advances in text-to-video generation have achieved impressive performance on short clips, yet evaluating long-form generation under complex textual inputs remains a significant challenge. In response to this challen…
arXiv cs.CL
TIER_1English(EN)·Blai Puchol, Sergio G\'omez Gonz\'alez, Miguel Domingo, Francisco Casacuberta·
arXiv:2605.29476v1 Announce Type: new Abstract: This work presents a comparative evaluation of machine translation systems applied to images containing textual information, a task that lies at the intersection of computer vision and natural language processing. The study compares…
Studying scalable methods for visual generative modeling requires large, accessible, and stable datasets. We introduce GPIC, a Giant Permissive Image Corpus of approximately 28 trillion pixels. GPIC comprises diverse internet images captioned by a state-of-the-art vision-language…
arXiv cs.AI
TIER_1English(EN)·Arian Komaei Koma, Seyed Amir Kasaei, AmirMahdi Sadeghzadeh, Mohammad Hossein Rohban·
arXiv:2605.26332v1 Announce Type: cross Abstract: Machine unlearning aims to remove specific concepts from pretrained text-to-image diffusion models, yet several white- and black-box attacks have been introduced to make the model generate such unlearned concepts. These attacks, n…
arXiv:2505.23606v5 Announce Type: replace Abstract: Unified generation models aim to handle diverse tasks across modalities -- such as text generation, image generation, and vision-language reasoning -- within a single architecture and decoding paradigm. Autoregressive unified mo…
arXiv:2510.22827v3 Announce Type: replace-cross Abstract: Evaluating text-to-image (T2I) systems requires judging not only whether an image matches a prompt, but also whether socially salient attributes are represented faithfully and without unsupported inference. Existing automa…
With the continued advancement of text-to-image (T2I) generation, producing high-quality images is becoming increasingly attainable; consequently, user demands are shifting toward images that better satisfy their specific requirements. As reward models play an increasingly import…
Lens is a compact 3.8B-parameter text-to-image model achieving superior performance with reduced training compute through dense caption datasets, multi-resolution batching, efficient architecture, and optimization techniques.
Discrete autoregressive text-to-image models suffer from latent covariate shift during policy optimization, which RankE addresses through end-to-end co-evolution of policy and decoder components.
arXiv:2606.05829v1 Announce Type: new Abstract: Artistic styles are rooted in specific socio-historical contexts that encode social hierarchies, including distinct constructions of gender. Yet in AI research, style has long been treated as a surface-level visual property: a filte…
Artistic styles are rooted in specific socio-historical contexts that encode social hierarchies, including distinct constructions of gender. Yet in AI research, style has long been treated as a surface-level visual property: a filter of color, brushstroke, and texture applied to …
arXiv:2606.04264v1 Announce Type: new Abstract: Recent years have seen remarkable progress in unified vision-language models handling both multimodal understanding and generation within a single architecture. While autoregressive VLMs can reason across modalities, they fail to ge…
arXiv:2606.03715v1 Announce Type: new Abstract: Text-to-image models rely on text prompts as their primary interface to human intent. Prompts are encoded by a text encoder into embeddings that condition the image generation process. Beyond individual token meanings, text embeddin…
Text-to-image models rely on text prompts as their primary interface to human intent. Prompts are encoded by a text encoder into embeddings that condition the image generation process. Beyond individual token meanings, text embeddings encode contextual information across the full…
arXiv cs.CV
TIER_1English(EN)·Lexi Pang, Liheng Zhang, Hang Ye, Xiaoxuan Ma, Yizhou Wang·
arXiv:2507.02792v5 Announce Type: replace Abstract: Text-to-image (T2I) diffusion models have shown remarkable success in generating high-quality images from text prompts. Recent efforts extend these models to incorporate conditional images (e.g., canny edge) for fine-grained spa…
arXiv:2412.03876v2 Announce Type: replace Abstract: Text-to-Image (T2I) diffusion models are widely recognized for their ability to generate high-quality and diverse images based on text prompts. However, despite recent advances, these models are still prone to generating unsafe …
arXiv cs.CV
TIER_1English(EN)·Jungmin Ko, Jungwon Park, Jimyeong Kim, Changin Choi, Wonseok Lee, Wonjong Rhee·
arXiv:2605.29390v1 Announce Type: new Abstract: Text-to-image (T2I) models have become increasingly capable of generating high-quality images. Yet, enforcing the explicit absence of a specified object or attribute remains a fundamentally challenging problem. Existing approaches, …
arXiv cs.CV
TIER_1English(EN)·Yexin Liu, Wen-Jie Shu, Zile Huang, Haoze Zheng, Yueze Wang, Jingjin Zhu, Manyuan Zhang, Ser-Nam Lim, Harry Yang·
arXiv:2512.01334v2 Announce Type: replace Abstract: Text-guided image-to-video generation has made substantial progress, yet it still struggles to execute text-specified edits that require substantial changes to a reference image (\textit{e.g., object addition, removal, or modifi…
arXiv:2604.03799v2 Announce Type: replace Abstract: Autoregressive (AR) models offer stable and efficient training, but standard next-token prediction is not well aligned with the temporal structure required for text-conditioned motion generation. We introduce MoScale, a next-sca…
arXiv:2605.28091v1 Announce Type: new Abstract: Text-to-Image generation has evolved from basic image synthesis into a frequently used core capability in professional creative workflows, where simple text-image alignment can no longer satisfy users' pressing demands for faithful …
Despite the rapid progress of text-to-image (T2I) models, generating images that accurately reflect complex compositional prompts (covering attribute bindings, object relationships, counting) still remains challenging. To address this, we propose BiDPO, a framework to enhance T2I…
Text-to-Image generation has evolved from basic image synthesis into a frequently used core capability in professional creative workflows, where simple text-image alignment can no longer satisfy users' pressing demands for faithful real-world reconstruction and genuine creative e…
arXiv cs.CV
TIER_1English(EN)·Yaofang Liu, Kangning Cui, Meng Chu, Zhaoqing Li, Suiyun Zhang, Jean-Michel Morel, Xiaodong Cun, Haoxuan Che, Rui Liu, Raymond H. Chan·
arXiv:2605.12271v2 Announce Type: replace Abstract: Humans often specify and create through visual artifacts: typography sheets, sketches, reference images, and annotated scenes. Yet modern visual generators still ask users to serialize this intent into text, a bottleneck that co…
arXiv:2605.25876v1 Announce Type: new Abstract: With the continued advancement of text-to-image (T2I) generation, producing high-quality images is becoming increasingly attainable; consequently, user demands are shifting toward images that better satisfy their specific requiremen…
arXiv:2605.25763v1 Announce Type: new Abstract: Text-to-image synthesis has made significant progress, benefiting from the strong generative capabilities of diffusion models. However, these models struggle to achieve precise text-to-image alignment within cross-attention maps dur…
arXiv cs.CV
TIER_1English(EN)·Shizhan Liu, Hao Zheng, Hang Yu, Jianguo Li·
arXiv:2503.01122v2 Announce Type: replace Abstract: Image personalization has garnered attention for its ability to customize Text-to-Image generation using only a few reference images. However, a key challenge in image personalization is the issue of conceptual coupling, where t…
With the continued advancement of text-to-image (T2I) generation, producing high-quality images is becoming increasingly attainable; consequently, user demands are shifting toward images that better satisfy their specific requirements. As reward models play an increasingly import…
Text-to-image synthesis has made significant progress, benefiting from the strong generative capabilities of diffusion models. However, these models struggle to achieve precise text-to-image alignment within cross-attention maps during the denoising process. Existing works primar…
arXiv:2605.21573v1 Announce Type: new Abstract: We introduce Lens, a 3.8B-parameter T2I model that achieves performance competitive with, and in several cases surpassing, state-of-the-art models with more than 6B parameters across various benchmarks, while requiring significantly…
Discrete autoregressive (AR) text-to-image (T2I) models pair a VQ tokenizer with an AR policy, and current post-training pipelines optimize only the policy while keeping the VQ decoder frozen. Recent diffusion T2I work, exemplified by REPA-E, has shown that the VAE itself constit…
