How JD’s Open‑Source JoyAI‑Echo Solves the Three Big Challenges of Long‑Form Video Generation

Technical Highlights

Cross‑modal Memory Bank

The framework embeds a dedicated memory bank that continuously stores and reuses each character’s visual appearance and voice timbre across multiple shots. In a 5‑minute video the identity, visual style, and voice remain highly consistent, eliminating the “changing face” problem.

Memory‑Driven Post‑Training

A post‑training pipeline combines Supervised Fine‑Tuning (SFT), cross‑modal Reinforcement Learning with Human Feedback (RLHF), and Distribution Matching Distillation (DMD). DMD alone contributes an approximate 7.5× inference‑speed boost, turning generation from minutes to seconds.

Director Agent

Users describe requirements in natural language; the system automatically decomposes them into script, characters, scenes, and shots. Only shots that fail quality checks are regenerated, avoiding full‑video re‑rendering. The workflow consists of planning, generation, review, and local revision, enabling conversational video editing.

Lightweight Real‑Time Super‑Resolution

A real‑time super‑resolution module offers two up‑sampling modes: 736×1280 → 1152×1920 and 736×1280 → 1472×2560 . Both modes deliver high‑resolution output without latency spikes, suitable for streaming constraints.

Comprehensive Evaluation

The team built a benchmark of 100 stories and 3,000 shots. JoyAI‑Echo outperforms competing models on cross‑shot consistency, video quality, text‑video alignment, and speech content accuracy, achieving a speech content accuracy of 0.8646 . User‑preference surveys report 81.7% favoring audio quality, 80.6% prompt adherence, 63.6% visual aesthetics, and 59.4% IP consistency.

Potential Applications

Virtual storytelling and animation production

Digital‑human content creation and live streaming

Rapid brand‑video iteration

Film pre‑visualization and storyboard generation

Interactive educational material

Game cut‑scene and narrative generation

Open‑Source Release

All code and model weights are publicly available at https://github.com/jd-opensource/JoyAI-Echo and the project homepage

https://echo-team-joy-future-academy-jd.github.io/Echo-LongVideo-Page/

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