The paper introduces the Real‑Calibrated Synthetic‑First Data Engine, a modular pipeline that integrates controllable diffusion‑based synthetic image generation with multi‑stage curation, filtering, and optional uncertainty‑driven selection and human verification. Designed as a CLI‑based framework, it allows independent configuration of generation, filtering, selection, and validation modules to enhance reproducibility and flexibility in real‑world data workflows. Empirical tests on human pose estimation demonstrate that synthetic data can boost a real‑data baseline when used as low‑cost augmentation, though synthetic‑only training still lags behind real‑only performance, underscoring the importance of data‑centric orchestration in low‑data regimes.
By Yukang Shen, Zhiguo Liu, Yingshu Li, Yan Huang
PoseDreamer is a new pipeline that uses diffusion models to generate large‑scale synthetic datasets for 3D human mesh estimation, providing 3D mesh annotations that remain aligned with the generated images. The system incorporates controllable image generation, Direct Preference Optimization for control alignment, curriculum‑based hard sample mining, and multi‑stage quality filtering to produce over 500,000 high‑quality samples with a 76% improvement in image‑quality metrics over traditional rendering‑based datasets. Models trained on PoseDreamer match or surpass those trained on real‑world or conventional synthetic data, and combining PoseDreamer with synthetic datasets yields better performance than mixing real and synthetic data alone.
By Lorenza Prospero, Orest Kupyn, Ostap Viniavskyi, Jo\~ao F. Henriques, Christian Rupprecht
arXiv:2606. 25128v1 Announce Type: cross Abstract: Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs.
By \"Umit Mert \c{C}a\u{g}lar, Alptekin Temizel
arXiv:2608. 02289v1 Announce Type: cross Abstract: Realistic and diverse trajectory generation is central to enabling higher levels of vehicle automation.
By Annajoyce Mariani, Kira Maag, Hanno Gottschalk
arXiv:2606. 16742v1 Announce Type: cross Abstract: With the rapid advancement of video generation models, distinguishing between AI-generated and authentic videos has emerged as a challenging endeavor.
By Renxi Cheng, Jie Gui, Hongsong Wang
The paper introduces a framework that adapts a diffusion model to a target urban domain using only imperfect pseudo‑labels, enabling the generation of high‑fidelity, target‑aligned images from semantic maps of any synthetic dataset. By filtering poor generations, correcting image‑label misalignments, and standardising semantics, the method transforms low‑effort synthetic data into competitive real‑domain training sets. Experiments on five synthetic and two real datasets show up to +8.0 %pt mIoU improvement over state‑of‑the‑art translation methods, demonstrating that rapidly constructed synthetic datasets can match the performance of high‑effort, manually designed ones.
By Damjan Kal\v{s}an, Denis Zavadski, Tim K\"uchler, Haebom Lee, Stefan Roth, Carsten Rother
arXiv:2606. 30528v1 Announce Type: cross Abstract: Current generative models, including GANs and diffusion models, have reached an outstanding level of photorealism, posing significant risks to privacy and security.
By Orazio Pontorno, Mattia Litrico, Luca Guarnera, Mario Valerio Giuffrida, Sebastiano Battiato
arXiv:2606. 08847v1 Announce Type: cross Abstract: Despite the success of image generation from text descriptions, it still faces challenges that are difficult to overcome in domains such as natural language processing (NLP) and computer vision (CV).
By Ahmed Abdelmoneim Mazrou, Haidy Maher El-Amir, Ali Hamdi
CogCanvas is a new benchmark for multi-subject reference-based image generation, featuring 1,952 curated reference images of 100 celebrities, 115 objects/fashion items, and 29 real-world backgrounds. It generates 1,361 compositional prompts with 2–5 people, using a pipeline that includes DINOv2 deduplication, aesthetic filtering, and automated graph derivation for interaction and positioning. The benchmark evaluates three tasks—reference-based multi-human-object generation, text-to-image compositional generation, and reference retrieval—under a six-axis protocol, and introduces BG‑Sim and Attr‑VQA metrics to assess background fidelity and attribute binding.
By Long-Bao Nguyen, Quang-Khai Le, Tam V. Nguyen, Minh-Triet Tran, Trung-Nghia Le
The paper introduces a post‑training approach that enables a single inference process to transition from text reasoning to image synthesis, eliminating the need for explicit modality switching. Using the 14B BAGEL model, the authors demonstrate that targeted post‑training data and reward‑weighted training improve multimodal image generation across four independent T2I benchmarks. The study highlights the benefits of joint text‑image generation and strategic data selection for enhancing T2I performance.
By Jiahui Chen, Philippe Hansen-Estruch, Xiaochuang Han, Yushi Hu, Emily Dinan, Amita Kamath, Michal Drozdzal, Reyhane Askari-Hemmat, Luke Zettlemoyer, Marjan Ghazvininejad
arXiv:2609.38010v1 Announce Type: cross
Abstract: Modern computer vision models achieve high accuracy when trained on large-scale annotated datasets. In critical domains such as construction safety m...
By Mohamed Benkedadra, Aissa Saoudi, Maxime Gloesener, Sidi Ahmed Mahmoudi, Matei Mancas
SolarWM is an open foundation for building interactive video world models, offering a reconfigurable multi‑source data engine that unifies 1.43 million clips from 10 datasets into a consistent, frame‑aligned format. It provides a backbone‑native adaptation framework that preserves native representations of models ranging from 5 B to 33 B parameters, and a three‑stage training recipe combining bidirectional adaptation, teacher‑forced autoregressive initialization, and distribution‑matching distillation. The resulting causal models can interact in real‑time over rollouts from minutes to hours, trained only on 5‑second sequences, and the project releases data, pipeline, recipes, weights, and framework for reproducible research.
By Junchao Huang, Guian Fang, Shengju Qian, Xianghao Kong, Zhuoran Zhao, Wei Huang, Yihua Du, Zixin Zhang, Justin Cui, Yuchao Gu, Yukang Chen, Xinting Hu, Tianyu He, Shaoshuai Shi, Zhuotao Tian, Xin Wang, Mike Zheng Shou, Li Jiang