How AI Dressing and Multimodal Models Transform Home Service Experiences

Technical Implementation and Industry Adaptation

DataFun: Could you share a typical technical challenge case for AI dressing?

Wang Mingzhong: Our first AI dressing trial in January 2024 used Stable Diffusion 1.5 with a LoRA trained on 58 Home Service uniforms and facial position detection. Diverse photo poses caused many generation failures and occasional body distortions, increasing manual review workload and halting progress.

In June, we adopted the open‑source flux.kontext from Black Forest Studio, adding facial preservation, person masks, and multimodal large‑model recognition to automatically discard unsuccessful dress‑up images, boosting success rates and reducing manual effort, enabling batch deployment for home‑service staff resumes.

DataFun: How do you balance visual quality and personalization for AI video résumés?

Wang Mingzhong: We first select a large model with minimal “AI‑style” artifacts, then apply a noise algorithm that mimics real‑photo grain, preserving each worker’s pose and physique to avoid uniform, overly generic outputs, thus achieving both realism and individuality.

Core Technology Architecture

DataFun: What algorithmic architecture powers the “one‑click video creation” template?

Wang Mingzhong: We maintain a rich video素材库 and multiple knowledge bases. When a user inputs a direction, the model selects the appropriate knowledge base to generate specialized content. A unified video protocol then assembles AI‑generated audio, video, text, and effects at the track level, enabling one‑click final composition.

DataFun: What breakthroughs enable realistic interaction in digital‑human live streams?

Wang Mingzhong: We collected extensive real‑world live‑room recordings to train speech habits, breathing patterns, and speaking rates, producing voices indistinguishable from human hosts. For interaction, the digital human finishes the current script line, then immediately replies to audience questions, preserving live flow and realism.

Industry Pain Points and Technical Solutions

DataFun: How does AI dressing handle non‑standard environments?

Wang Mingzhong: We define standard dressing photo criteria and filter out non‑conforming images. The large model’s inherent capabilities cover most challenging cases, while a multimodal model assists in reviewing and discarding imperfect results, effectively tackling non‑standard scenarios.

DataFun: How does AI video résumé improve decision efficiency through multimodal data fusion?

Wang Mingzhong: We extract and train on extensive voice recordings of domestic workers, generate a natural‑sounding self‑introduction, combine it with their photos using multimodal capabilities, and produce an AI video résumé that lets users quickly assess candidates.

Commercial Value and Industry Transformation

DataFun: How does modular design and “pay‑as‑you‑go” help SMEs control costs?

Wang Mingzhong: Our AI video platform modularizes capabilities (voice, AI‑generated video, audio effects, text effects, synthesis). When new demands arise, we recombine existing modules for rapid solutions. Users can also select from various capability packages that suit their needs, from full‑stack creation to simple one‑click templates.

DataFun: How do you prevent user loss during dual‑track technology upgrades?

Wang Mingzhong: We retain traditional service channels and ensure their quality for existing users, while gradually guiding them to new technology via demo pages and effect videos that showcase tangible benefits, encouraging adoption without abandoning legacy users.

Future Technology Evolution

DataFun: What is the next generation direction for short‑video templates?

Wang Mingzhong: Currently, two approaches exist: fixed‑template creation offering stable, market‑validated results, and agent‑driven creation offering freeform content but with higher randomness. Future fusion will let agents autonomously select mature templates, fetch required assets, and combine them with generative capabilities for richer, more reliable outputs.

DataFun: Will emotional interaction become a key metric for digital‑human live streams, and how can its value be quantified?

Wang Mingzhong: Emotional interaction is indeed a next‑stage KPI. Current digital humans lack deep emotional expression, relying mainly on generated video and voice. Long‑term, they should display real‑time facial expressions, gestures, and behaviors aligned with content needs, eliminating the “AI feel” and delivering genuine user value.