Alexa+ devices use animated icons to reinforce spoken content, but the original hand‑drawn icons were too slow to scale. A new automated pipeline now generates vectorised animated icons from text, breaking the process into image generation, semantic decomposition, vectorisation, styling, and animation. The key improvement is a reinforcement‑learning‑based fine‑tuning of the image generation stage using objective verifiers, boosting the pass rate from 15.7% to 58.7% under an independent VLM judge.
arXiv:2606.04457v2 Announce Type: replace Abstract: Incorporating visual semantic representations as an intermediate step before image generation can reduce the modeling difficulty between text and i...
Back2Struct is a system that converts structured images—such as diagrams, charts, and flowcharts—into editable vector graphics code (SVG/XML). By predicting semantically rich, object-level SVG code rather than low-level pixel vectorization, it allows the generated graphics to be imported into tools like PowerPoint for easy editing, restyling, and reuse. The model is trained with supervised fine‑tuning and reward‑based learning that enforces syntactic validity, concise length, and visual fidelity to the input, leading to higher accuracy, editability, and user alignment compared to baselines.
Structured images, such as diagrams, charts, and flowcharts, are inherently symbolic and can be compactly represented in an editable format, yet in practice, they are often rendered as images, and the...
arXiv:2610.18860v1 Announce Type: new Abstract: Generating information-rich visual artifacts such as slides, posters, and dashboards as code is appealing because the outputs are structured, editable,...
arXiv:2510.12041v3 Announce Type: replace Abstract: Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models often struggle with simple or underspecifie...
VGA‑BenchV2 is an expanded, human‑aligned benchmark and optimization framework that jointly evaluates video generation quality and aesthetic value. It builds on the original VGA‑Bench taxonomy, adding 52 sub‑dimensions and 1,016 curated prompts to generate over 60,000 videos from 12 mainstream models. The benchmark significantly enlarges human supervision with 36,000 task‑level annotations and introduces a hybrid evaluator (VAQA‑Net, VTag‑Net, VGQA‑Net) that aligns well with human judgments and can be used as a reward model for reinforcement‑learning fine‑tuning.
arXiv:2607. 01709v1 Announce Type: new Abstract: Agents are increasingly used to construct workflows and assist humans in completing recurring tasks more efficiently.
arXiv:2608. 09666v1 Announce Type: new Abstract: Recent advances in visual generative models have enabled high-quality image and video generation, but evaluating these models often demands sampling hundreds or thousands of images or videos, which is computationally expensive.
arXiv:2610.20603v1 Announce Type: new Abstract: We present CommerceGen-Studio, an end-to-end framework for generating commercial-grade main images for e-commerce Stock Keeping Units (SKUs). Our frame...
We present CommerceGen-Studio, an end-to-end framework for generating commercial-grade main images for e-commerce Stock Keeping Units (SKUs). Our framework is organized along a three-stage pipeline of...
We introduce VGA-BenchV2, an extended human-aligned benchmark and optimization framework for jointly evaluating and improving video generation quality and aesthetic value. Built upon VGA-Bench, VGA-Be...
Recent image generators have demonstrated impressive photorealism and instruction-following capabilities in single-image generation and editing. However, constrained by their architectures, they cannot achieve interleaved generation (text-image sequence), which has crucial applications in visual narratives, guidance, and embodied manipulation.