How AI Dressing and Digital Humans Are Revolutionizing Home Service Experiences

01 Technical Implementation and Industry Adaptation

Wang explains the first attempt at AI dressing in January 2024 using Stable Diffusion 1.5 and a LoRA trained on 58 Home service uniforms, combined with face‑position detection. Diverse photo poses caused generation failures and occasional body distortions, increasing manual review workload.

In June, the team adopted flux.kontext from Black Forest Studio, adding face preservation, person masks, and multimodal large‑model recognition to automatically discard unsuccessful images, improving success rate and reducing manual effort, now applied at scale to 58 Home service resumes.

02 Core Technical Architecture

For AI video resumes, the pipeline extracts voice recordings of domestic workers, trains a voice model, generates a conversational self‑introduction, and combines it with the worker’s photo using multimodal capabilities to produce an AI video resume, enhancing user decision efficiency.

Short‑video “one‑click creation” relies on a large multimedia asset library and multiple knowledge bases. The system selects the appropriate knowledge base based on user input, then uses a unified video protocol to compose audio, video, text, and effects at the track level, enabling rapid generation of professional videos.

Digital‑human live streaming achieves realistic speech by training on extensive real‑world broadcast recordings, mimicking breathing, speaking speed, and intonation. Interaction is handled by having the digital human finish the current script line before responding to audience questions, preserving live‑stream continuity.

03 Industry Pain‑Points and Technical Solutions

AI dressing tackles non‑standard environments by defining a standard photo criteria, discarding non‑conforming images, leveraging large‑model capabilities to handle challenging cases, and using multimodal models for automated quality review.

Modular design and “pay‑as‑you‑go” enable SMEs to combine various AI video capabilities (voice synthesis, AI‑generated video, sound effects, text effects, video compositing) into flexible packages, allowing users to select the features that match their needs.

04 Business Value and Industry Transformation

Modular AI video solutions allow rapid assembly of new features without rebuilding from scratch, supporting cost‑effective innovation for small and medium enterprises.

05 Future Technology Evolution

Future short‑video generation may blend template‑based creation with agent‑driven freedom, where agents autonomously select mature templates, gather required assets, and incorporate the latest popular templates, representing the next generation of technology fusion.

Emotional interaction will become a key metric for digital‑human live streaming; quantifying value will involve measuring the realism of facial expressions, gestures, and behaviors to eliminate the “AI feel” and deliver truly human‑like experiences.