Real-Time Neural Video Rendering for Games

# Illustrative sketch (not from the paper)
import torch
from typing import List, Tuple

# 1. Game engine produces white‑box frames (RGB tensor) and camera poses
def engine_step(state) -> Tuple[torch.Tensor, Tuple[float, float, float]]:
    white_box = torch.randn(3, 224, 224)  # placeholder frame
    pose = (0.0, 0.0, 0.0)                # placeholder camera pose
    return white_box, pose

# 2. Render server loads the visual backbone (Wan2.2‑TI2V‑5B)
class RenderServer:
    def __init__(self, text_prompt: str, first_image: torch.Tensor):
        self.model = torch.hub.load('wanrepo/Wan2.2-TI2V-5B', 'model')
        self.text_prompt = text_prompt
        self.history: List[torch.Tensor] = [first_image]

    # 3. Denoising conditioned on white‑box frame and retrieved history
    def render(self, white_box: torch.Tensor, pose: Tuple[float, float, float]) -> torch.Tensor:
        # retrieve recent frames based on pose (simplified as last history entry)
        context = self.history[-1]
        # combine white‑box and context as conditioning input
        cond = torch.cat([white_box, context], dim=0).unsqueeze(0)  # [1, C, H, W]
        # forward through the diffusion model (placeholder call)
        with torch.no_grad():
            rendered = self.model(cond, self.text_prompt)
        # update short‑term memory
        self.history.append(rendered.squeeze(0))
        if len(self.history) > 5:
            self.history.pop(0)
        return rendered.squeeze(0)

# 4. Main loop (illustrative, not real‑time)
if __name__ == "__main__":
    state = {}
    first_img = torch.randn(3, 224, 224)  # initial visual observation
    server = RenderServer(text_prompt="fantasy forest", first_image=first_img)
    for _ in range(10):
        wb_frame, cam_pose = engine_step(state)
        final_vis = server.render(wb_frame, cam_pose)
        # send final_vis to client (omitted)

// Illustrative sketch (not from the paper)
const torch = require('torch-js'); // placeholder binding

// 1. Engine emits white‑box frame and camera pose
function engineStep(state) {
  const whiteBox = torch.randn([3, 224, 224]); // placeholder tensor
  const pose = { x: 0, y: 0, z: 0 }; // placeholder pose
  return { whiteBox, pose };
}

// 2. Render server loads Wan2.2‑TI2V‑5B model
class RenderServer {
  constructor(textPrompt, firstImage) {
    this.model = torch.hub.load('wanrepo/Wan2.2-TI2V-5B', 'model');
    this.textPrompt = textPrompt;
    this.history = [firstImage];
  }

  // 3. Render using denoising conditioned on white‑box and recent history
  render(whiteBox, pose) {
    const context = this.history[this.history.length - 1];
    const cond = torch.cat([whiteBox, context], 0).unsqueeze(0); // [1, C, H, W]
    const rendered = this.model.forward(cond, this.textPrompt); // placeholder call
    // update short‑term memory (keep last 5 frames)
    this.history.push(rendered.squeeze(0));
    if (this.history.length > 5) this.history.shift();
    return rendered.squeeze(0);
  }
}

// 4. Main loop (illustrative)
(async () => {
  const state = {};
  const firstImg = torch.randn([3, 224, 224]);
  const server = new RenderServer('fantasy forest', firstImg);
  for (let i = 0; i < 10; i++) {
    const { whiteBox, pose } = engineStep(state);
    const finalVis = server.render(whiteBox, pose);
    // send finalVis to client (omitted)
  }
})();